From 1b6e2108d5f997ea63423bb6130e3c137ed05ffe Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sat, 11 Dec 2021 20:55:57 +0000 Subject: [PATCH 01/27] Basic template for tutorial --- ...vantage_in_learning_from_experiments.ipynb | 206 ++++++++++++++++++ 1 file changed, 206 insertions(+) create mode 100644 docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb new file mode 100644 index 000000000..3e63797ee --- /dev/null +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xLOXFOT5Q40E" + }, + "source": [ + "##### Copyright 2020 The TensorFlow Authors." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "cellView": "form", + "colab": {}, + "colab_type": "code", + "id": "iiQkM5ZgQ8r2" + }, + "outputs": [], + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "j6331ZSsQGY3" + }, + "source": [ + "# Quantum advantage in learning from experiments" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i9Jcnb8bQQyd" + }, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " View on TensorFlow.org\n", + " \n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + " \n", + " Download notebook\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6tYn2HaAUgH0" + }, + "source": [ + "This tutorial shows the experiments of Quantum advantage in learning from experiments." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "sPZoNKvpUaqa" + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "TorxE5tnkvb2" + }, + "outputs": [], + "source": [ + "!pip install tensorflow==2.4.1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FxkQA6oblNqI" + }, + "source": [ + "Install TensorFlow Quantum:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "saFHsRDpkvkH" + }, + "outputs": [], + "source": [ + "!pip install tensorflow-quantum" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4Ql5PW-ACO0J" + }, + "outputs": [], + "source": [ + "# Update package resources to account for version changes.\n", + "import importlib, pkg_resources\n", + "importlib.reload(pkg_resources)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "F1L8h1YKUvIO" + }, + "source": [ + "Now import TensorFlow and the module dependencies:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "enZ300Bflq80" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import tensorflow_quantum as tfq\n", + "\n", + "import cirq" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b08Mmbs8lr81" + }, + "source": [ + "## 1. The Basics" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "quantum_advantage_in_learning_from_experiments.ipynb", + "private_outputs": true, + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From be15d639f6dfcb88850dcbae232f50003207bf28 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 19 Dec 2021 16:40:55 +0000 Subject: [PATCH 02/27] Add Sycamore gate JSON --- docs/tutorials/syc_un_bell_pair.json | 165 +++++++++++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 docs/tutorials/syc_un_bell_pair.json diff --git a/docs/tutorials/syc_un_bell_pair.json b/docs/tutorials/syc_un_bell_pair.json new file mode 100644 index 000000000..6f4575fd6 --- /dev/null +++ b/docs/tutorials/syc_un_bell_pair.json @@ -0,0 +1,165 @@ +{ + "cirq_type": "Circuit", + "moments": [ + { + "cirq_type": "Moment", + "operations": [ + { + "cirq_type": "GateOperation", + "gate": { + "cirq_type": "PhasedXZGate", + "axis_phase_exponent": 0.9986250945802118, + "x_exponent": 0.5863459345742326, + "z_exponent": -0.6536106799786454 + }, + "qubits": [ + { + "cirq_type": "GridQubit", + "row": 0, + "col": 0 + } + ] + }, + { + "cirq_type": "GateOperation", + "gate": { + "cirq_type": "PhasedXZGate", + "axis_phase_exponent": 0.5000000000000218, + "x_exponent": 0.5000000000000004, + "z_exponent": 0.632026098311071 + }, + "qubits": [ + { + "cirq_type": "GridQubit", + "row": 0, + "col": 1 + } + ] + } + ] + }, + { + "cirq_type": "Moment", + "operations": [ + { + "cirq_type": "GateOperation", + "gate": { + "cirq_type": "SycamoreGate" + }, + "qubits": [ + { + "cirq_type": "GridQubit", + "row": 0, + "col": 0 + }, + { + "cirq_type": "GridQubit", + "row": 0, + "col": 1 + } + ] + } + ] + }, + { + "cirq_type": "Moment", + "operations": [ + { + "cirq_type": "GateOperation", + "gate": { + "cirq_type": "PhasedXZGate", + "axis_phase_exponent": 0.5473086105824576, + "x_exponent": 8.393286066166183e-14, + "z_exponent": 0.8593680884394623 + }, + "qubits": [ + { + "cirq_type": "GridQubit", + "row": 0, + "col": 0 + } + ] + }, + { + "cirq_type": "GateOperation", + "gate": { + "cirq_type": "PhasedXZGate", + "axis_phase_exponent": 0.2616810812682333, + "x_exponent": 0.47712669849899525, + "z_exponent": 0.6530681770222657 + }, + "qubits": [ + { + "cirq_type": "GridQubit", + "row": 0, + "col": 1 + } + ] + } + ] + }, + { + "cirq_type": "Moment", + "operations": [ + { + "cirq_type": "GateOperation", + "gate": { + "cirq_type": "SycamoreGate" + }, + "qubits": [ + { + "cirq_type": "GridQubit", + "row": 0, + "col": 0 + }, + { + "cirq_type": "GridQubit", + "row": 0, + "col": 1 + } + ] + } + ] + }, + { + "cirq_type": "Moment", + "operations": [ + { + "cirq_type": "GateOperation", + "gate": { + "cirq_type": "PhasedXZGate", + "axis_phase_exponent": -0.17371487943054031, + "x_exponent": 0.9136433894458278, + "z_exponent": -0.32134194944999406 + }, + "qubits": [ + { + "cirq_type": "GridQubit", + "row": 0, + "col": 0 + } + ] + }, + { + "cirq_type": "GateOperation", + "gate": { + "cirq_type": "PhasedXZGate", + "axis_phase_exponent": -0.6752724799161121, + "x_exponent": 0.49999999999997824, + "z_exponent": 0.17527247991611183 + }, + "qubits": [ + { + "cirq_type": "GridQubit", + "row": 0, + "col": 1 + } + ] + } + ] + } + ], + "device": { + "cirq_type": "_UnconstrainedDevice" + } +} From a7695626cfe756d48a803a175083b3fcc5f23b21 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 19 Dec 2021 16:56:02 +0000 Subject: [PATCH 03/27] Intermediate --- ...vantage_in_learning_from_experiments.ipynb | 183 ++++++++++++++++-- 1 file changed, 171 insertions(+), 12 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 3e63797ee..071fdb3a3 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": null, "metadata": { "cellView": "form", "colab": {}, @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": 15, "metadata": { "colab": {}, "colab_type": "code", @@ -159,20 +159,179 @@ "outputs": [], "source": [ "import tensorflow as tf\n", - "import tensorflow_quantum as tfq\n", - "\n", - "import cirq" + "import cirq\n", + "from cirq_google import SycamoreGate\n", + "import numpy as np\n", + "import sympy\n", + "import functools\n", + "import os" ] }, { "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "b08Mmbs8lr81" - }, + "metadata": {}, "source": [ "## 1. The Basics" ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "@functools.lru_cache(maxsize=128)\n", + "def _load_circuit(fname: str) -> cirq.Circuit:\n", + " with open(fname, \"r\") as f:\n", + " return cirq.read_json(f)\n", + "\n", + "def un_bell_pair_block(qubits):\n", + " \"\"\"Un compute a bell pair on two qubits.\n", + "\n", + " Enacts CNOT(a, b) + H(a) using SycamoreGates and single qubit operations.\n", + "\n", + " Args:\n", + " qubits: The qubits to un-prepare the bell pair on.\n", + "\n", + " Returns:\n", + " A list of `cirq.Operations` realizing the operation.\n", + " \"\"\"\n", + " mapped_circuit = _load_circuit(\"syc_un_bell_pair.json\").transform_qubits(\n", + " {cirq.GridQubit(0, 0): qubits[0], cirq.GridQubit(0, 1): qubits[1]}\n", + " )\n", + " return mapped_circuit.all_operations()\n", + "\n", + "def inv_z_basis_gate(pauli):\n", + " \"\"\"Returns inverse Z basis transformation ops for a given Pauli.\n", + "\n", + " Args:\n", + " pauli: Python str representing a single pauli.\n", + "\n", + " Returns:\n", + " Corresponding `cirq.Gate` to do the inverse basis conversion.\n", + " \"\"\"\n", + " if pauli == \"I\" or pauli == \"Z\":\n", + " return cirq.I\n", + " if pauli == \"X\":\n", + " return cirq.H\n", + " if pauli == \"Y\":\n", + " # S^dag H to get to computational, H S to go back.\n", + " return cirq.PhasedXZGate(\n", + " axis_phase_exponent=-0.5, x_exponent=0.5, z_exponent=-0.5\n", + " )\n", + " raise ValueError(\"Invalid Pauli.\")\n", + "\n", + "def flatten_circuit(circuit: cirq.Circuit) -> cirq.Circuit:\n", + " \"\"\"Pack operations in circuit to the left as far as possible.\n", + "\n", + " Args:\n", + " circuit: `cirq.Circuit` who's operations will be packed.\n", + "\n", + " Returns:\n", + " A `cirq.Circuit` with operations packed to the left as\n", + " far as possible.\n", + " \"\"\"\n", + " return cirq.Circuit([op for mom in circuit for op in mom])\n", + "\n", + " \n", + "def build_circuit(\n", + " qubit_pairs,\n", + " pauli,\n", + " n_shots,\n", + " rand_state):\n", + " \"\"\"Create I + P problem circuit between qubit pairs.\n", + "\n", + " Args:\n", + " qubit_pairs: List of qubit pairs.\n", + " pauli: Python str containing characters 'I', 'X', 'Y' or 'Z'.\n", + " n_shots: Number of repetitions to generate for sweeps.\n", + "\n", + " Returns:\n", + " A (circuit, sweep) tuple, runnable using `run_sweep`.\n", + " \"\"\"\n", + " a_qubits = [pair[0] for pair in qubit_pairs]\n", + " b_qubits = [pair[1] for pair in qubit_pairs]\n", + " all_qubits = np.concatenate(qubit_pairs)\n", + "\n", + " flip_params = sympy.symbols(f\"param_0:{len(qubit_pairs) * 2}\")\n", + "\n", + " # Add X flips.\n", + " ret_circuit = cirq.Circuit(cirq.X(q) ** p for q, p in zip(all_qubits, flip_params))\n", + "\n", + " # Add basis turns a and b.\n", + " ret_circuit += [\n", + " inv_z_basis_gate(p)(q) for q, p in zip(a_qubits, pauli)\n", + " ]\n", + " ret_circuit += [\n", + " inv_z_basis_gate(p)(q) for q, p in zip(b_qubits, pauli)\n", + " ]\n", + "\n", + " # Add un-bell pair.\n", + " ret_circuit += [un_bell_pair_block(pair) for pair in qubit_pairs]\n", + "\n", + " # Add measurements.\n", + " for i, qubit in enumerate(all_qubits):\n", + " ret_circuit += cirq.measure(qubit, key=f\"q{i}\")\n", + "\n", + " # Merge single qubit operations, flatten moments and align measurements.\n", + " cirq.merge_single_qubit_gates_into_phxz(ret_circuit)\n", + " cirq.DropEmptyMoments().optimize_circuit(circuit=ret_circuit)\n", + " ret_circuit = flatten_circuit(ret_circuit)\n", + " cirq.SynchronizeTerminalMeasurements().optimize_circuit(circuit=ret_circuit)\n", + "\n", + " # Create randomized flippings. These flippings will contain values of 1,0.\n", + " # which will turn the X gates on or off.\n", + " params = circuit_blocks.create_randomized_sweeps(\n", + " pauli, flip_params, n_shots, rand_state\n", + " )\n", + " logging.debug(\n", + " f\"Generated circuit w/ depth {len(ret_circuit)} and {len(params)} sweeps.\"\n", + " )\n", + " return ret_circuit, params\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['I' 'I' 'Z']\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'circuit_blocks' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpauli\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mcircuit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msweeps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_circuit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msystem_pairs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpauli\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_shots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrand_source\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mbuild_circuit\u001b[0;34m(qubit_pairs, pauli, n_shots, rand_state)\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;31m# Create randomized flippings. These flippings will contain values of 1,0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;31m# which will turn the X gates on or off.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m params = circuit_blocks.create_randomized_sweeps(\n\u001b[0m\u001b[1;32m 103\u001b[0m \u001b[0mpauli\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflip_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_shots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrand_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'circuit_blocks' is not defined" + ] + } + ], + "source": [ + "rand_source = np.random.RandomState(1234)\n", + "n_paulis = 7\n", + "n = 3\n", + "n_shots = 100\n", + "\n", + "paulis = np.array([\"X\", \"Y\", \"Z\", \"I\"])\n", + "pauli_strings = rand_source.choice(a=paulis, size=(n_paulis, n), replace=True)\n", + "\n", + "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", + "\n", + "for pauli in pauli_strings:\n", + " print(pauli)\n", + " \n", + " circuit, sweeps = build_circuit(system_pairs, pauli, n_shots, rand_source)" + ] } ], "metadata": { From 0028447f4e1cd830ed56fcf2e2c783e508fd3203 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Fri, 24 Dec 2021 03:32:54 +0000 Subject: [PATCH 04/27] Intermediate results --- ...vantage_in_learning_from_experiments.ipynb | 234 +++++++++++++++--- 1 file changed, 203 insertions(+), 31 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 071fdb3a3..bb361e79c 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", @@ -176,10 +176,40 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ + "def create_randomized_sweeps(\n", + " hidden_p,\n", + " symbols,\n", + " n_params,\n", + " rand_state):\n", + " last_i = 0\n", + " for i, pauli in enumerate(hidden_p):\n", + " if pauli != \"I\":\n", + " last_i = i\n", + "\n", + " sign_p = rand_state.choice([1, -1])\n", + " all_sweeps = []\n", + " for _ in range(n_params):\n", + " current_sweep = dict()\n", + " for twocopy in [0, 1]:\n", + " parity = sign_p * rand_state.choice([1, -1], p=[0.95, 0.05])\n", + " for i, pauli in enumerate(hidden_p):\n", + " current_symbol = symbols[2 * i + twocopy]\n", + " current_sweep[current_symbol] = rand_state.choice([0, 1])\n", + " if pauli != \"I\":\n", + " if last_i == i:\n", + " v = 1 if parity == -1 else 0\n", + " current_sweep[current_symbol] = v\n", + " elif current_sweep[current_symbol] == 1:\n", + " parity *= -1\n", + "\n", + " all_sweeps.append(current_sweep)\n", + " return all_sweeps\n", + "\n", + "\n", "@functools.lru_cache(maxsize=128)\n", "def _load_circuit(fname: str) -> cirq.Circuit:\n", " with open(fname, \"r\") as f:\n", @@ -281,56 +311,198 @@ "\n", " # Create randomized flippings. These flippings will contain values of 1,0.\n", " # which will turn the X gates on or off.\n", - " params = circuit_blocks.create_randomized_sweeps(\n", + " params = create_randomized_sweeps(\n", " pauli, flip_params, n_shots, rand_state\n", " )\n", - " logging.debug(\n", - " f\"Generated circuit w/ depth {len(ret_circuit)} and {len(params)} sweeps.\"\n", - " )\n", " return ret_circuit, params\n", "\n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "rand_source = np.random.RandomState(1234)\n", + "n_paulis = 89\n", + "n = 3\n", + "n_shots = 50\n", + "n_sweeps = 10\n", + "\n", + "paulis = np.array([\"X\", \"Y\", \"Z\", \"I\"])\n", + "pauli_strings = rand_source.choice(a=paulis, size=(n_paulis, n), replace=True)\n", + "\n", + "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", + "\n", + "simulator = cirq.Simulator()\n", + "\n", + "all_results = []\n", + "for pauli in pauli_strings:\n", + " circuit, sweeps = build_circuit(system_pairs, pauli, n_shots, rand_source)\n", + " \n", + " results_for_pauli = []\n", + " for b in range(0, n_shots, n_sweeps):\n", + " results = simulator.run_sweep(\n", + " program=circuit,\n", + " params=sweeps[b : b + n_sweeps],\n", + " repetitions=1\n", + " )\n", + "\n", + " batch_results = []\n", + " for j, single_circuit_samples in enumerate(results):\n", + " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", + " out0 = single_circuit_samples.data[qubit_order].to_numpy()\n", + " batch_results.append(np.squeeze(out0))\n", + "\n", + " batch_results = np.array(batch_results)\n", + " results_for_pauli.append(batch_results)\n", + " all_results.append(np.concatenate(results_for_pauli))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def _encode_pauli(paulis):\n", + " encoded = []\n", + " for pauli in paulis:\n", + " if pauli == 'I':\n", + " encoded.extend([0, 0])\n", + " elif pauli == 'X':\n", + " encoded.extend([0, 1])\n", + " elif pauli == 'Y':\n", + " encoded.extend([1, 0])\n", + " elif pauli == 'Z':\n", + " encoded.extend([1, 1])\n", + " return np.asarray([encoded])\n", + " \n", + "inputs = []\n", + "targets = []\n", + " \n", + "for i in range(n_paulis):\n", + " encoded_pauli = np.repeat(_encode_pauli(pauli_strings[i, :]), n_shots, axis=0)\n", + " \n", + " inputs.append(np.expand_dims(np.concatenate((all_results[i], encoded_pauli,), axis=1), axis=0))\n", + " targets.append(1)\n", + " \n", + " inputs.append(np.expand_dims(np.concatenate((all_results[(i + 1) % len(all_results)], encoded_pauli,), axis=1), axis=0))\n", + " targets.append(0)\n", + "\n", + "inputs = np.concatenate(inputs)\n", + "targets = np.asarray(targets)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['I' 'I' 'Z']\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'circuit_blocks' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpauli\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mcircuit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msweeps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_circuit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msystem_pairs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpauli\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_shots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrand_source\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mbuild_circuit\u001b[0;34m(qubit_pairs, pauli, n_shots, rand_state)\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;31m# Create randomized flippings. These flippings will contain values of 1,0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;31m# which will turn the X gates on or off.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m params = circuit_blocks.create_randomized_sweeps(\n\u001b[0m\u001b[1;32m 103\u001b[0m \u001b[0mpauli\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflip_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_shots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrand_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m )\n", - "\u001b[0;31mNameError\u001b[0m: name 'circuit_blocks' is not defined" + "Epoch 1/30\n", + "6/6 [==============================] - 6s 74ms/step - loss: 0.4420\n", + "Epoch 2/30\n", + "6/6 [==============================] - 0s 70ms/step - loss: 0.3898\n", + "Epoch 3/30\n", + "6/6 [==============================] - 0s 74ms/step - loss: 0.3499\n", + "Epoch 4/30\n", + "6/6 [==============================] - 0s 74ms/step - loss: 0.3158\n", + "Epoch 5/30\n", + "6/6 [==============================] - 0s 75ms/step - loss: 0.2917\n", + "Epoch 6/30\n", + "6/6 [==============================] - 0s 76ms/step - loss: 0.2758\n", + "Epoch 7/30\n", + "6/6 [==============================] - 0s 78ms/step - loss: 0.2651\n", + "Epoch 8/30\n", + "6/6 [==============================] - 0s 73ms/step - loss: 0.2583\n", + "Epoch 9/30\n", + "6/6 [==============================] - 0s 73ms/step - loss: 0.2546\n", + "Epoch 10/30\n", + "6/6 [==============================] - 0s 72ms/step - loss: 0.2540\n", + "Epoch 11/30\n", + "6/6 [==============================] - 0s 71ms/step - loss: 0.2525\n", + "Epoch 12/30\n", + "6/6 [==============================] - 0s 72ms/step - loss: 0.2524\n", + "Epoch 13/30\n", + "6/6 [==============================] - 0s 72ms/step - loss: 0.2523\n", + "Epoch 14/30\n", + "6/6 [==============================] - 0s 70ms/step - loss: 0.2523\n", + "Epoch 15/30\n", + "6/6 [==============================] - 0s 71ms/step - loss: 0.2523\n", + "Epoch 16/30\n", + "6/6 [==============================] - 0s 70ms/step - loss: 0.2522\n", + "Epoch 17/30\n", + "6/6 [==============================] - 0s 73ms/step - loss: 0.2521\n", + "Epoch 18/30\n", + "6/6 [==============================] - 0s 70ms/step - loss: 0.2520\n", + "Epoch 19/30\n", + "6/6 [==============================] - 0s 68ms/step - loss: 0.2520\n", + "Epoch 20/30\n", + "6/6 [==============================] - 0s 69ms/step - loss: 0.2520\n", + "Epoch 21/30\n", + "6/6 [==============================] - 0s 71ms/step - loss: 0.2519\n", + "Epoch 22/30\n", + "6/6 [==============================] - 0s 69ms/step - loss: 0.2518\n", + "Epoch 23/30\n", + "6/6 [==============================] - 0s 71ms/step - loss: 0.2517\n", + "Epoch 24/30\n", + "6/6 [==============================] - 0s 71ms/step - loss: 0.2517\n", + "Epoch 25/30\n", + "6/6 [==============================] - 0s 71ms/step - loss: 0.2522\n", + "Epoch 26/30\n", + "6/6 [==============================] - 0s 70ms/step - loss: 0.2517\n", + "Epoch 27/30\n", + "6/6 [==============================] - 0s 69ms/step - loss: 0.2515\n", + "Epoch 28/30\n", + "6/6 [==============================] - 0s 72ms/step - loss: 0.2515\n", + "Epoch 29/30\n", + "6/6 [==============================] - 0s 73ms/step - loss: 0.2514\n", + "Epoch 30/30\n", + "6/6 [==============================] - 0s 72ms/step - loss: 0.2514\n" ] } ], "source": [ - "rand_source = np.random.RandomState(1234)\n", - "n_paulis = 7\n", - "n = 3\n", - "n_shots = 100\n", + "model = tf.keras.Sequential()\n", + "model.add(tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True))\n", + "model.add(tf.keras.layers.GRU(4, go_backwards=True, return_sequences=True))\n", + "model.add(tf.keras.layers.GRU(1, go_backwards=False))\n", "\n", - "paulis = np.array([\"X\", \"Y\", \"Z\", \"I\"])\n", - "pauli_strings = rand_source.choice(a=paulis, size=(n_paulis, n), replace=True)\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n", + "loss = tf.keras.losses.MeanSquaredError()\n", "\n", - "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", + "model.compile(optimizer=optimizer, loss=loss)\n", "\n", - "for pauli in pauli_strings:\n", - " print(pauli)\n", - " \n", - " circuit, sweeps = build_circuit(system_pairs, pauli, n_shots, rand_source)" + "history = model.fit(\n", + " x=inputs.astype(float),\n", + " y=targets.astype(float),\n", + " epochs=30,\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy=0.5337078651685393\n" + ] + } + ], + "source": [ + "predictions = np.squeeze(model(inputs).numpy() > 0.5) == (targets == 1)\n", + "accuracy = sum(predictions) / len(predictions) \n", + "print(f\"accuracy={accuracy}\")" ] } ], From 420d65746ec470de8095c0f43f8e3b82af878e60 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Fri, 24 Dec 2021 14:01:36 +0000 Subject: [PATCH 05/27] intermediate --- ...vantage_in_learning_from_experiments.ipynb | 80 ++++----- docs/tutorials/syc_un_bell_pair.json | 165 ------------------ 2 files changed, 32 insertions(+), 213 deletions(-) delete mode 100644 docs/tutorials/syc_un_bell_pair.json diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index bb361e79c..962b83501 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "colab": {}, "colab_type": "code", @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -210,26 +210,9 @@ " return all_sweeps\n", "\n", "\n", - "@functools.lru_cache(maxsize=128)\n", - "def _load_circuit(fname: str) -> cirq.Circuit:\n", - " with open(fname, \"r\") as f:\n", - " return cirq.read_json(f)\n", "\n", "def un_bell_pair_block(qubits):\n", - " \"\"\"Un compute a bell pair on two qubits.\n", - "\n", - " Enacts CNOT(a, b) + H(a) using SycamoreGates and single qubit operations.\n", - "\n", - " Args:\n", - " qubits: The qubits to un-prepare the bell pair on.\n", - "\n", - " Returns:\n", - " A list of `cirq.Operations` realizing the operation.\n", - " \"\"\"\n", - " mapped_circuit = _load_circuit(\"syc_un_bell_pair.json\").transform_qubits(\n", - " {cirq.GridQubit(0, 0): qubits[0], cirq.GridQubit(0, 1): qubits[1]}\n", - " )\n", - " return mapped_circuit.all_operations()\n", + " return [cirq.CNOT(qubits[0], qubits[1]), cirq.H(qubits[0])]\n", "\n", "def inv_z_basis_gate(pauli):\n", " \"\"\"Returns inverse Z basis transformation ops for a given Pauli.\n", @@ -250,19 +233,6 @@ " axis_phase_exponent=-0.5, x_exponent=0.5, z_exponent=-0.5\n", " )\n", " raise ValueError(\"Invalid Pauli.\")\n", - "\n", - "def flatten_circuit(circuit: cirq.Circuit) -> cirq.Circuit:\n", - " \"\"\"Pack operations in circuit to the left as far as possible.\n", - "\n", - " Args:\n", - " circuit: `cirq.Circuit` who's operations will be packed.\n", - "\n", - " Returns:\n", - " A `cirq.Circuit` with operations packed to the left as\n", - " far as possible.\n", - " \"\"\"\n", - " return cirq.Circuit([op for mom in circuit for op in mom])\n", - "\n", " \n", "def build_circuit(\n", " qubit_pairs,\n", @@ -303,12 +273,6 @@ " for i, qubit in enumerate(all_qubits):\n", " ret_circuit += cirq.measure(qubit, key=f\"q{i}\")\n", "\n", - " # Merge single qubit operations, flatten moments and align measurements.\n", - " cirq.merge_single_qubit_gates_into_phxz(ret_circuit)\n", - " cirq.DropEmptyMoments().optimize_circuit(circuit=ret_circuit)\n", - " ret_circuit = flatten_circuit(ret_circuit)\n", - " cirq.SynchronizeTerminalMeasurements().optimize_circuit(circuit=ret_circuit)\n", - "\n", " # Create randomized flippings. These flippings will contain values of 1,0.\n", " # which will turn the X gates on or off.\n", " params = create_randomized_sweeps(\n", @@ -320,15 +284,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "rand_source = np.random.RandomState(1234)\n", - "n_paulis = 89\n", - "n = 3\n", - "n_shots = 50\n", - "n_sweeps = 10\n", + "rand_source = np.random.RandomState(20160913)\n", + "n_paulis = 2\n", + "n = 1\n", + "n_shots = 1\n", + "n_sweeps = 1\n", "\n", "paulis = np.array([\"X\", \"Y\", \"Z\", \"I\"])\n", "pauli_strings = rand_source.choice(a=paulis, size=(n_paulis, n), replace=True)\n", @@ -363,9 +327,27 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[[0, 0, 1, 1]],\n", + " \n", + " [[1, 0, 1, 1]],\n", + " \n", + " [[1, 0, 0, 1]],\n", + " \n", + " [[0, 0, 0, 1]]]),\n", + " array([1, 0, 1, 0]))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def _encode_pauli(paulis):\n", " encoded = []\n", @@ -393,7 +375,9 @@ " targets.append(0)\n", "\n", "inputs = np.concatenate(inputs)\n", - "targets = np.asarray(targets)\n" + "targets = np.asarray(targets)\n", + "\n", + "inputs, targets\n" ] }, { diff --git a/docs/tutorials/syc_un_bell_pair.json b/docs/tutorials/syc_un_bell_pair.json deleted file mode 100644 index 6f4575fd6..000000000 --- a/docs/tutorials/syc_un_bell_pair.json +++ /dev/null @@ -1,165 +0,0 @@ -{ - "cirq_type": "Circuit", - "moments": [ - { - "cirq_type": "Moment", - "operations": [ - { - "cirq_type": "GateOperation", - "gate": { - "cirq_type": "PhasedXZGate", - "axis_phase_exponent": 0.9986250945802118, - "x_exponent": 0.5863459345742326, - "z_exponent": -0.6536106799786454 - }, - "qubits": [ - { - "cirq_type": "GridQubit", - "row": 0, - "col": 0 - } - ] - }, - { - "cirq_type": "GateOperation", - "gate": { - "cirq_type": "PhasedXZGate", - "axis_phase_exponent": 0.5000000000000218, - "x_exponent": 0.5000000000000004, - "z_exponent": 0.632026098311071 - }, - "qubits": [ - { - "cirq_type": "GridQubit", - "row": 0, - "col": 1 - } - ] - } - ] - }, - { - "cirq_type": "Moment", - "operations": [ - { - "cirq_type": "GateOperation", - "gate": { - "cirq_type": "SycamoreGate" - }, - "qubits": [ - { - "cirq_type": "GridQubit", - "row": 0, - "col": 0 - }, - { - "cirq_type": "GridQubit", - "row": 0, - "col": 1 - } - ] - } - ] - }, - { - "cirq_type": "Moment", - "operations": [ - { - "cirq_type": "GateOperation", - "gate": { - "cirq_type": "PhasedXZGate", - "axis_phase_exponent": 0.5473086105824576, - "x_exponent": 8.393286066166183e-14, - "z_exponent": 0.8593680884394623 - }, - "qubits": [ - { - "cirq_type": "GridQubit", - "row": 0, - "col": 0 - } - ] - }, - { - "cirq_type": "GateOperation", - "gate": { - "cirq_type": "PhasedXZGate", - "axis_phase_exponent": 0.2616810812682333, - "x_exponent": 0.47712669849899525, - "z_exponent": 0.6530681770222657 - }, - "qubits": [ - { - "cirq_type": "GridQubit", - "row": 0, - "col": 1 - } - ] - } - ] - }, - { - "cirq_type": "Moment", - "operations": [ - { - "cirq_type": "GateOperation", - "gate": { - "cirq_type": "SycamoreGate" - }, - "qubits": [ - { - "cirq_type": "GridQubit", - "row": 0, - "col": 0 - }, - { - "cirq_type": "GridQubit", - "row": 0, - "col": 1 - } - ] - } - ] - }, - { - "cirq_type": "Moment", - "operations": [ - { - "cirq_type": "GateOperation", - "gate": { - "cirq_type": "PhasedXZGate", - "axis_phase_exponent": -0.17371487943054031, - "x_exponent": 0.9136433894458278, - "z_exponent": -0.32134194944999406 - }, - "qubits": [ - { - "cirq_type": "GridQubit", - "row": 0, - "col": 0 - } - ] - }, - { - "cirq_type": "GateOperation", - "gate": { - "cirq_type": "PhasedXZGate", - "axis_phase_exponent": -0.6752724799161121, - "x_exponent": 0.49999999999997824, - "z_exponent": 0.17527247991611183 - }, - "qubits": [ - { - "cirq_type": "GridQubit", - "row": 0, - "col": 1 - } - ] - } - ] - } - ], - "device": { - "cirq_type": "_UnconstrainedDevice" - } -} From ebba738bb55e93596179993bf151fac638286ce5 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 26 Dec 2021 00:02:00 +0000 Subject: [PATCH 06/27] intermediate --- ...vantage_in_learning_from_experiments.ipynb | 149 ++++++++---------- 1 file changed, 63 insertions(+), 86 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 962b83501..b3a0b4b84 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -327,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -335,15 +335,18 @@ "text/plain": [ "(array([[[0, 0, 1, 1]],\n", " \n", - " [[1, 0, 1, 1]],\n", + " [[1, 1, 1, 1]],\n", " \n", - " [[1, 0, 0, 1]],\n", + " [[1, 1, 0, 1]],\n", " \n", " [[0, 0, 0, 1]]]),\n", - " array([1, 0, 1, 0]))" + " array([[1, 0],\n", + " [0, 1],\n", + " [1, 0],\n", + " [0, 1]]))" ] }, - "execution_count": 11, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -369,10 +372,10 @@ " encoded_pauli = np.repeat(_encode_pauli(pauli_strings[i, :]), n_shots, axis=0)\n", " \n", " inputs.append(np.expand_dims(np.concatenate((all_results[i], encoded_pauli,), axis=1), axis=0))\n", - " targets.append(1)\n", + " targets.append([1, 0])\n", " \n", " inputs.append(np.expand_dims(np.concatenate((all_results[(i + 1) % len(all_results)], encoded_pauli,), axis=1), axis=0))\n", - " targets.append(0)\n", + " targets.append([0, 1])\n", "\n", "inputs = np.concatenate(inputs)\n", "targets = np.asarray(targets)\n", @@ -382,92 +385,70 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/30\n", - "6/6 [==============================] - 6s 74ms/step - loss: 0.4420\n", - "Epoch 2/30\n", - "6/6 [==============================] - 0s 70ms/step - loss: 0.3898\n", - "Epoch 3/30\n", - "6/6 [==============================] - 0s 74ms/step - loss: 0.3499\n", - "Epoch 4/30\n", - "6/6 [==============================] - 0s 74ms/step - loss: 0.3158\n", - "Epoch 5/30\n", - "6/6 [==============================] - 0s 75ms/step - loss: 0.2917\n", - "Epoch 6/30\n", - "6/6 [==============================] - 0s 76ms/step - loss: 0.2758\n", - "Epoch 7/30\n", - "6/6 [==============================] - 0s 78ms/step - loss: 0.2651\n", - "Epoch 8/30\n", - "6/6 [==============================] - 0s 73ms/step - loss: 0.2583\n", - "Epoch 9/30\n", - "6/6 [==============================] - 0s 73ms/step - loss: 0.2546\n", - "Epoch 10/30\n", - "6/6 [==============================] - 0s 72ms/step - loss: 0.2540\n", - "Epoch 11/30\n", - "6/6 [==============================] - 0s 71ms/step - loss: 0.2525\n", - "Epoch 12/30\n", - "6/6 [==============================] - 0s 72ms/step - loss: 0.2524\n", - "Epoch 13/30\n", - "6/6 [==============================] - 0s 72ms/step - loss: 0.2523\n", - "Epoch 14/30\n", - "6/6 [==============================] - 0s 70ms/step - loss: 0.2523\n", - "Epoch 15/30\n", - "6/6 [==============================] - 0s 71ms/step - loss: 0.2523\n", - "Epoch 16/30\n", - "6/6 [==============================] - 0s 70ms/step - loss: 0.2522\n", - "Epoch 17/30\n", - "6/6 [==============================] - 0s 73ms/step - loss: 0.2521\n", - "Epoch 18/30\n", - "6/6 [==============================] - 0s 70ms/step - loss: 0.2520\n", - "Epoch 19/30\n", - "6/6 [==============================] - 0s 68ms/step - loss: 0.2520\n", - "Epoch 20/30\n", - "6/6 [==============================] - 0s 69ms/step - loss: 0.2520\n", - "Epoch 21/30\n", - "6/6 [==============================] - 0s 71ms/step - loss: 0.2519\n", - "Epoch 22/30\n", - "6/6 [==============================] - 0s 69ms/step - loss: 0.2518\n", - "Epoch 23/30\n", - "6/6 [==============================] - 0s 71ms/step - loss: 0.2517\n", - "Epoch 24/30\n", - "6/6 [==============================] - 0s 71ms/step - loss: 0.2517\n", - "Epoch 25/30\n", - "6/6 [==============================] - 0s 71ms/step - loss: 0.2522\n", - "Epoch 26/30\n", - "6/6 [==============================] - 0s 70ms/step - loss: 0.2517\n", - "Epoch 27/30\n", - "6/6 [==============================] - 0s 69ms/step - loss: 0.2515\n", - "Epoch 28/30\n", - "6/6 [==============================] - 0s 72ms/step - loss: 0.2515\n", - "Epoch 29/30\n", - "6/6 [==============================] - 0s 73ms/step - loss: 0.2514\n", - "Epoch 30/30\n", - "6/6 [==============================] - 0s 72ms/step - loss: 0.2514\n" + "(4, 1)\n", + "(4, 2)\n", + "(None, 2)\n", + "0.5\n", + "0.6945574283599854\n", + "1.0\n", + "3.3853211789391935e-06\n" ] } ], "source": [ "model = tf.keras.Sequential()\n", - "model.add(tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True))\n", - "model.add(tf.keras.layers.GRU(4, go_backwards=True, return_sequences=True))\n", - "model.add(tf.keras.layers.GRU(1, go_backwards=False))\n", + "#model.add(tf.keras.Input(shape=(4, 1)))\n", + "model.add(tf.keras.layers.Dense(2, activation='linear', use_bias=True))\n", + "model.add(tf.keras.layers.Dense(8, activation='relu', use_bias=True))\n", + "model.add(tf.keras.layers.Dense(8, activation='relu', use_bias=True))\n", + "model.add(tf.keras.layers.Dense(2, activation='softmax', use_bias=True))\n", + "\n", + "x = inputs[:, :, 1] + inputs[:, :, 2]\n", + "y = targets # np.expand_dims(targets, axis=1)\n", "\n", - "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n", - "loss = tf.keras.losses.MeanSquaredError()\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=0.005)\n", + "loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n", "\n", - "model.compile(optimizer=optimizer, loss=loss)\n", + "model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n", "\n", "history = model.fit(\n", - " x=inputs.astype(float),\n", - " y=targets.astype(float),\n", - " epochs=30,\n", - " verbose=1)" + " x=x.astype(float),\n", + " y=y.astype(float),\n", + " epochs=5000,\n", + " verbose=0)\n", + "\n", + "print(x.shape)\n", + "print(y.shape)\n", + "print(model.output_shape)\n", + "print(history.history['accuracy'][0])\n", + "print(history.history['loss'][0])\n", + "print(history.history['accuracy'][-1])\n", + "print(history.history['loss'][-1])\n", + "#print(model(x))\n", + "#print(model.summary())\n", + "\n", + "# model = tf.keras.Sequential()\n", + "# # model.add(tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True))\n", + "# # model.add(tf.keras.layers.GRU(4, go_backwards=True, return_sequences=True))\n", + "# model.add(tf.keras.layers.GRU(1, go_backwards=False))\n", + "\n", + "# optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n", + "# loss = tf.keras.losses.MeanSquaredError()\n", + "\n", + "# model.compile(optimizer=optimizer, loss=loss)\n", + "\n", + "# history = model.fit(\n", + "# x=inputs.astype(float),\n", + "# y=targets.astype(float),\n", + "# epochs=30,\n", + "# verbose=1)" ] }, { @@ -483,11 +464,7 @@ ] } ], - "source": [ - "predictions = np.squeeze(model(inputs).numpy() > 0.5) == (targets == 1)\n", - "accuracy = sum(predictions) / len(predictions) \n", - "print(f\"accuracy={accuracy}\")" - ] + "source": [] } ], "metadata": { From aced112d17ddef710e84bc72ecacd330b73f2e6b Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 26 Dec 2021 00:22:05 +0000 Subject: [PATCH 07/27] intermediate --- ...vantage_in_learning_from_experiments.ipynb | 79 ++++--------------- 1 file changed, 17 insertions(+), 62 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index b3a0b4b84..ce08f92c6 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -284,14 +284,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "rand_source = np.random.RandomState(20160913)\n", "n_paulis = 2\n", - "n = 1\n", - "n_shots = 1\n", + "n = 3\n", + "n_shots = 47\n", "n_sweeps = 1\n", "\n", "paulis = np.array([\"X\", \"Y\", \"Z\", \"I\"])\n", @@ -327,26 +327,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([[[0, 0, 1, 1]],\n", - " \n", - " [[1, 1, 1, 1]],\n", - " \n", - " [[1, 1, 0, 1]],\n", - " \n", - " [[0, 0, 0, 1]]]),\n", - " array([[1, 0],\n", - " [0, 1],\n", - " [1, 0],\n", - " [0, 1]]))" + "((4, 47, 12), (4, 2))" ] }, - "execution_count": 4, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -380,12 +370,12 @@ "inputs = np.concatenate(inputs)\n", "targets = np.asarray(targets)\n", "\n", - "inputs, targets\n" + "inputs.shape, targets.shape\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -396,32 +386,30 @@ "(4, 2)\n", "(None, 2)\n", "0.5\n", - "0.6945574283599854\n", + "0.6981427669525146\n", "1.0\n", - "3.3853211789391935e-06\n" + "0.00026620164862833917\n" ] } ], "source": [ "model = tf.keras.Sequential()\n", - "#model.add(tf.keras.Input(shape=(4, 1)))\n", - "model.add(tf.keras.layers.Dense(2, activation='linear', use_bias=True))\n", "model.add(tf.keras.layers.Dense(8, activation='relu', use_bias=True))\n", + "model.add(tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True))\n", + "model.add(tf.keras.layers.GRU(4, go_backwards=True, return_sequences=True))\n", + "model.add(tf.keras.layers.GRU(4, go_backwards=False))\n", "model.add(tf.keras.layers.Dense(8, activation='relu', use_bias=True))\n", "model.add(tf.keras.layers.Dense(2, activation='softmax', use_bias=True))\n", "\n", - "x = inputs[:, :, 1] + inputs[:, :, 2]\n", - "y = targets # np.expand_dims(targets, axis=1)\n", - "\n", "optimizer = tf.keras.optimizers.Adam(learning_rate=0.005)\n", "loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n", "\n", "model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n", "\n", "history = model.fit(\n", - " x=x.astype(float),\n", - " y=y.astype(float),\n", - " epochs=5000,\n", + " x=inputs.astype(float),\n", + " y=targets.astype(float),\n", + " epochs=500,\n", " verbose=0)\n", "\n", "print(x.shape)\n", @@ -430,41 +418,8 @@ "print(history.history['accuracy'][0])\n", "print(history.history['loss'][0])\n", "print(history.history['accuracy'][-1])\n", - "print(history.history['loss'][-1])\n", - "#print(model(x))\n", - "#print(model.summary())\n", - "\n", - "# model = tf.keras.Sequential()\n", - "# # model.add(tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True))\n", - "# # model.add(tf.keras.layers.GRU(4, go_backwards=True, return_sequences=True))\n", - "# model.add(tf.keras.layers.GRU(1, go_backwards=False))\n", - "\n", - "# optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n", - "# loss = tf.keras.losses.MeanSquaredError()\n", - "\n", - "# model.compile(optimizer=optimizer, loss=loss)\n", - "\n", - "# history = model.fit(\n", - "# x=inputs.astype(float),\n", - "# y=targets.astype(float),\n", - "# epochs=30,\n", - "# verbose=1)" + "print(history.history['loss'][-1])\n" ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "accuracy=0.5337078651685393\n" - ] - } - ], - "source": [] } ], "metadata": { From 34c724fd89a4c0a04c12fb346afab9df53e5727c Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 26 Dec 2021 00:35:17 +0000 Subject: [PATCH 08/27] intermediate --- ...vantage_in_learning_from_experiments.ipynb | 995 +++++++++++++++++- 1 file changed, 981 insertions(+), 14 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index ce08f92c6..384b5f7f0 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -284,12 +284,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "rand_source = np.random.RandomState(20160913)\n", - "n_paulis = 2\n", + "n_paulis = 200\n", "n = 3\n", "n_shots = 47\n", "n_sweeps = 1\n", @@ -327,16 +327,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "((4, 47, 12), (4, 2))" + "((400, 47, 12), (400, 2))" ] }, - "execution_count": 21, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -375,20 +375,987 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(4, 1)\n", - "(4, 2)\n", - "(None, 2)\n", - "0.5\n", - "0.6981427669525146\n", - "1.0\n", - "0.00026620164862833917\n" + "Epoch 1/500\n", + "13/13 [==============================] - 6s 62ms/step - loss: 0.6949 - accuracy: 0.4975\n", + "Epoch 2/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.6941 - accuracy: 0.4850\n", + "Epoch 3/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6934 - accuracy: 0.4925\n", + "Epoch 4/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6934 - accuracy: 0.4950\n", + "Epoch 5/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.5000\n", + "Epoch 6/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.5000\n", + "Epoch 7/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.6932 - accuracy: 0.4900\n", + "Epoch 8/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6932 - accuracy: 0.5275\n", + "Epoch 9/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.4975\n", + "Epoch 10/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.5100\n", + "Epoch 11/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6931 - accuracy: 0.5050\n", + "Epoch 12/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.6935 - accuracy: 0.4950\n", + "Epoch 13/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.5000\n", + "Epoch 14/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6932 - accuracy: 0.5000\n", + "Epoch 15/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6931 - accuracy: 0.4975\n", + "Epoch 16/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6931 - accuracy: 0.4900\n", + "Epoch 17/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6931 - accuracy: 0.5075\n", + "Epoch 18/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6926 - accuracy: 0.5200\n", + "Epoch 19/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6946 - accuracy: 0.4475\n", + "Epoch 20/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6935 - accuracy: 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accuracy: 0.9900\n", + "Epoch 445/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0328 - accuracy: 0.9875\n", + "Epoch 446/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0325 - accuracy: 0.9875\n", + "Epoch 447/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0365 - accuracy: 0.9875\n", + "Epoch 448/500\n", + "13/13 [==============================] - 1s 61ms/step - loss: 0.0292 - accuracy: 0.9900\n", + "Epoch 449/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0316 - accuracy: 0.9825\n", + "Epoch 450/500\n", + "13/13 [==============================] - 1s 61ms/step - loss: 0.0367 - accuracy: 0.9850\n", + "Epoch 451/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0737 - accuracy: 0.9700\n", + "Epoch 452/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0906 - accuracy: 0.9675\n", + "Epoch 453/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.1040 - accuracy: 0.9650\n", + "Epoch 454/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0683 - accuracy: 0.9750\n", + "Epoch 455/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0583 - accuracy: 0.9775\n", + "Epoch 456/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0549 - accuracy: 0.9775\n", + "Epoch 457/500\n", + "13/13 [==============================] - 1s 61ms/step - loss: 0.0505 - accuracy: 0.9825\n", + "Epoch 458/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0683 - accuracy: 0.9700\n", + "Epoch 459/500\n", + "13/13 [==============================] - 1s 61ms/step - loss: 0.0831 - accuracy: 0.9675\n", + "Epoch 460/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0637 - accuracy: 0.9750\n", + "Epoch 461/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0562 - accuracy: 0.9775\n", + "Epoch 462/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0396 - accuracy: 0.9900\n", + "Epoch 463/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0341 - accuracy: 0.9875\n", + "Epoch 464/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0364 - accuracy: 0.9825\n", + "Epoch 465/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.0443 - accuracy: 0.9875\n", + "Epoch 466/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0388 - accuracy: 0.9875\n", + "Epoch 467/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0362 - accuracy: 0.9850\n", + "Epoch 468/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0306 - accuracy: 0.9875\n", + "Epoch 469/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0289 - accuracy: 0.9850\n", + "Epoch 470/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0286 - accuracy: 0.9900\n", + "Epoch 471/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.0307 - accuracy: 0.9850\n", + "Epoch 472/500\n", + " 3/13 [=====>........................] - ETA: 0s - loss: 0.0252 - accuracy: 0.9896" ] } ], @@ -410,7 +1377,7 @@ " x=inputs.astype(float),\n", " y=targets.astype(float),\n", " epochs=500,\n", - " verbose=0)\n", + " verbose=1)\n", "\n", "print(x.shape)\n", "print(y.shape)\n", From 2d2851aa177591dc4331735ad16224bc285b9558 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 26 Dec 2021 00:37:21 +0000 Subject: [PATCH 09/27] intermediate --- ...vantage_in_learning_from_experiments.ipynb | 73 ++++++++++++++++++- 1 file changed, 71 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 384b5f7f0..1e7d286e3 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -375,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1355,7 +1355,76 @@ "Epoch 471/500\n", "13/13 [==============================] - 1s 63ms/step - loss: 0.0307 - accuracy: 0.9850\n", "Epoch 472/500\n", - " 3/13 [=====>........................] - ETA: 0s - loss: 0.0252 - accuracy: 0.9896" + "13/13 [==============================] - 1s 65ms/step - loss: 0.0289 - accuracy: 0.9850\n", + "Epoch 473/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.0247 - accuracy: 0.9925\n", + "Epoch 474/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.0333 - accuracy: 0.9875\n", + "Epoch 475/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.0299 - accuracy: 0.9875\n", + "Epoch 476/500\n", + "13/13 [==============================] - 1s 64ms/step - loss: 0.0275 - accuracy: 0.9875\n", + "Epoch 477/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0281 - accuracy: 0.9900\n", + "Epoch 478/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0269 - accuracy: 0.9925\n", + "Epoch 479/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.0254 - accuracy: 0.9875\n", + "Epoch 480/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.0295 - accuracy: 0.9900\n", + "Epoch 481/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.1777 - accuracy: 0.9400\n", + "Epoch 482/500\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 [==============================] - 1s 61ms/step - loss: 0.3427 - accuracy: 0.9025\n", + "Epoch 483/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.9733 - accuracy: 0.7825\n", + "Epoch 484/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.8728 - accuracy: 0.7025\n", + "Epoch 485/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.7868 - accuracy: 0.7600\n", + "Epoch 486/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.8302 - accuracy: 0.7100\n", + "Epoch 487/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.7626 - accuracy: 0.7025\n", + "Epoch 488/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.6386 - accuracy: 0.7450\n", + "Epoch 489/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.4899 - accuracy: 0.8000\n", + "Epoch 490/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.4116 - accuracy: 0.8075\n", + "Epoch 491/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.4566 - accuracy: 0.7950\n", + "Epoch 492/500\n", + "13/13 [==============================] - 1s 63ms/step - loss: 0.4242 - accuracy: 0.8050\n", + "Epoch 493/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.3539 - accuracy: 0.8275\n", + "Epoch 494/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.3796 - accuracy: 0.8250\n", + "Epoch 495/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.3135 - accuracy: 0.8525\n", + "Epoch 496/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.2849 - accuracy: 0.8750\n", + "Epoch 497/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.2645 - accuracy: 0.8850\n", + "Epoch 498/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.2395 - accuracy: 0.9050\n", + "Epoch 499/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.2344 - accuracy: 0.9075\n", + "Epoch 500/500\n", + "13/13 [==============================] - 1s 62ms/step - loss: 0.2175 - accuracy: 0.9175\n", + "(4, 1)\n", + "(4, 2)\n", + "(None, 2)\n", + "0.4975000023841858\n", + "0.694918692111969\n", + "0.9175000190734863\n", + "0.21751846373081207\n" ] } ], From e364cd2257abe3bdb5fd9c02c50f996f5e4d04cb Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 26 Dec 2021 15:10:49 +0000 Subject: [PATCH 10/27] intermediate --- ...vantage_in_learning_from_experiments.ipynb | 1114 +---------------- 1 file changed, 47 insertions(+), 1067 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 1e7d286e3..dc5c105be 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -160,11 +160,9 @@ "source": [ "import tensorflow as tf\n", "import cirq\n", - "from cirq_google import SycamoreGate\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import sympy\n", - "import functools\n", - "import os" + "import sympy" ] }, { @@ -284,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -327,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -336,7 +334,7 @@ "((400, 47, 12), (400, 2))" ] }, - "execution_count": 29, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -375,1059 +373,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/500\n", - "13/13 [==============================] - 6s 62ms/step - loss: 0.6949 - accuracy: 0.4975\n", - "Epoch 2/500\n", - "13/13 [==============================] - 1s 63ms/step - loss: 0.6941 - accuracy: 0.4850\n", - "Epoch 3/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6934 - accuracy: 0.4925\n", - "Epoch 4/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6934 - accuracy: 0.4950\n", - "Epoch 5/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.5000\n", - "Epoch 6/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.5000\n", - "Epoch 7/500\n", - "13/13 [==============================] - 1s 63ms/step - loss: 0.6932 - accuracy: 0.4900\n", - "Epoch 8/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6932 - accuracy: 0.5275\n", - "Epoch 9/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.4975\n", - "Epoch 10/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.5100\n", - "Epoch 11/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6931 - accuracy: 0.5050\n", - "Epoch 12/500\n", - "13/13 [==============================] - 1s 63ms/step - loss: 0.6935 - accuracy: 0.4950\n", - "Epoch 13/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6933 - accuracy: 0.5000\n", - "Epoch 14/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6932 - accuracy: 0.5000\n", - "Epoch 15/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6931 - accuracy: 0.4975\n", - "Epoch 16/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6931 - accuracy: 0.4900\n", - "Epoch 17/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6931 - accuracy: 0.5075\n", - "Epoch 18/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6926 - accuracy: 0.5200\n", - "Epoch 19/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6946 - accuracy: 0.4475\n", - "Epoch 20/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6935 - accuracy: 0.5075\n", - "Epoch 21/500\n", - "13/13 [==============================] - 1s 61ms/step - 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accuracy: 0.7025\n", - "Epoch 488/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.6386 - accuracy: 0.7450\n", - "Epoch 489/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.4899 - accuracy: 0.8000\n", - "Epoch 490/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.4116 - accuracy: 0.8075\n", - "Epoch 491/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.4566 - accuracy: 0.7950\n", - "Epoch 492/500\n", - "13/13 [==============================] - 1s 63ms/step - loss: 0.4242 - accuracy: 0.8050\n", - "Epoch 493/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.3539 - accuracy: 0.8275\n", - "Epoch 494/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.3796 - accuracy: 0.8250\n", - "Epoch 495/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.3135 - accuracy: 0.8525\n", - "Epoch 496/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.2849 - accuracy: 0.8750\n", - "Epoch 497/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.2645 - accuracy: 0.8850\n", - "Epoch 498/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.2395 - accuracy: 0.9050\n", - "Epoch 499/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.2344 - accuracy: 0.9075\n", - "Epoch 500/500\n", - "13/13 [==============================] - 1s 62ms/step - loss: 0.2175 - accuracy: 0.9175\n", - "(4, 1)\n", - "(4, 2)\n", - "(None, 2)\n", - "0.4975000023841858\n", - "0.694918692111969\n", - "0.9175000190734863\n", - "0.21751846373081207\n" - ] - } - ], + "outputs": [], "source": [ "model = tf.keras.Sequential()\n", "model.add(tf.keras.layers.Dense(8, activation='relu', use_bias=True))\n", @@ -1446,15 +394,47 @@ " x=inputs.astype(float),\n", " y=targets.astype(float),\n", " epochs=500,\n", - " verbose=1)\n", + " verbose=0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['accuracy'], 'b')\n", + "plt.ylabel('accuracy')\n", + "plt.show()\n", "\n", - "print(x.shape)\n", - "print(y.shape)\n", - "print(model.output_shape)\n", - "print(history.history['accuracy'][0])\n", - "print(history.history['loss'][0])\n", - "print(history.history['accuracy'][-1])\n", - "print(history.history['loss'][-1])\n" + "plt.plot(history.history['loss'], 'r')\n", + "plt.ylabel('loss')\n", + "plt.show()\n" ] } ], From c051d101a258f8961c44f086e3db3d42c3fbe350 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 26 Dec 2021 16:14:20 +0000 Subject: [PATCH 11/27] Lower LR, some text --- ...vantage_in_learning_from_experiments.ipynb | 147 +++++++++++------- 1 file changed, 90 insertions(+), 57 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index dc5c105be..492f76231 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -172,55 +172,23 @@ "## 1. The Basics" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first define the circuit we are going to use to generate samples" + ] + }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "def create_randomized_sweeps(\n", - " hidden_p,\n", - " symbols,\n", - " n_params,\n", - " rand_state):\n", - " last_i = 0\n", - " for i, pauli in enumerate(hidden_p):\n", - " if pauli != \"I\":\n", - " last_i = i\n", - "\n", - " sign_p = rand_state.choice([1, -1])\n", - " all_sweeps = []\n", - " for _ in range(n_params):\n", - " current_sweep = dict()\n", - " for twocopy in [0, 1]:\n", - " parity = sign_p * rand_state.choice([1, -1], p=[0.95, 0.05])\n", - " for i, pauli in enumerate(hidden_p):\n", - " current_symbol = symbols[2 * i + twocopy]\n", - " current_sweep[current_symbol] = rand_state.choice([0, 1])\n", - " if pauli != \"I\":\n", - " if last_i == i:\n", - " v = 1 if parity == -1 else 0\n", - " current_sweep[current_symbol] = v\n", - " elif current_sweep[current_symbol] == 1:\n", - " parity *= -1\n", - "\n", - " all_sweeps.append(current_sweep)\n", - " return all_sweeps\n", - "\n", - "\n", - "\n", "def un_bell_pair_block(qubits):\n", " return [cirq.CNOT(qubits[0], qubits[1]), cirq.H(qubits[0])]\n", "\n", "def inv_z_basis_gate(pauli):\n", - " \"\"\"Returns inverse Z basis transformation ops for a given Pauli.\n", - "\n", - " Args:\n", - " pauli: Python str representing a single pauli.\n", - "\n", - " Returns:\n", - " Corresponding `cirq.Gate` to do the inverse basis conversion.\n", - " \"\"\"\n", " if pauli == \"I\" or pauli == \"Z\":\n", " return cirq.I\n", " if pauli == \"X\":\n", @@ -237,16 +205,6 @@ " pauli,\n", " n_shots,\n", " rand_state):\n", - " \"\"\"Create I + P problem circuit between qubit pairs.\n", - "\n", - " Args:\n", - " qubit_pairs: List of qubit pairs.\n", - " pauli: Python str containing characters 'I', 'X', 'Y' or 'Z'.\n", - " n_shots: Number of repetitions to generate for sweeps.\n", - "\n", - " Returns:\n", - " A (circuit, sweep) tuple, runnable using `run_sweep`.\n", - " \"\"\"\n", " a_qubits = [pair[0] for pair in qubit_pairs]\n", " b_qubits = [pair[1] for pair in qubit_pairs]\n", " all_qubits = np.concatenate(qubit_pairs)\n", @@ -280,11 +238,61 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we create the samples." + ] + }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], + "source": [ + "def create_randomized_sweeps(\n", + " hidden_p,\n", + " symbols,\n", + " n_params,\n", + " rand_state):\n", + " last_i = 0\n", + " for i, pauli in enumerate(hidden_p):\n", + " if pauli != \"I\":\n", + " last_i = i\n", + "\n", + " sign_p = rand_state.choice([1, -1])\n", + " all_sweeps = []\n", + " for _ in range(n_params):\n", + " current_sweep = dict()\n", + " for twocopy in [0, 1]:\n", + " parity = sign_p * rand_state.choice([1, -1], p=[0.95, 0.05])\n", + " for i, pauli in enumerate(hidden_p):\n", + " current_symbol = symbols[2 * i + twocopy]\n", + " current_sweep[current_symbol] = rand_state.choice([0, 1])\n", + " if pauli != \"I\":\n", + " if last_i == i:\n", + " v = 1 if parity == -1 else 0\n", + " current_sweep[current_symbol] = v\n", + " elif current_sweep[current_symbol] == 1:\n", + " parity *= -1\n", + "\n", + " all_sweeps.append(current_sweep)\n", + " return all_sweeps\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We run the code" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ "rand_source = np.random.RandomState(20160913)\n", "n_paulis = 200\n", @@ -325,7 +333,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "We encode the results so that they can be ingested by our neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -334,7 +351,7 @@ "((400, 47, 12), (400, 2))" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -371,9 +388,16 @@ "inputs.shape, targets.shape\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We train the model" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -385,7 +409,7 @@ "model.add(tf.keras.layers.Dense(8, activation='relu', use_bias=True))\n", "model.add(tf.keras.layers.Dense(2, activation='softmax', use_bias=True))\n", "\n", - "optimizer = tf.keras.optimizers.Adam(learning_rate=0.005)\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0005)\n", "loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n", "\n", "model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n", @@ -393,18 +417,27 @@ "history = model.fit(\n", " x=inputs.astype(float),\n", " y=targets.astype(float),\n", - " epochs=500,\n", + " epochs=1000,\n", " verbose=0)\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] From 637f4ac231c6bd0d735c33d8d73bacc2372ff065 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 2 Jan 2022 16:17:34 +0000 Subject: [PATCH 12/27] conjoined network --- ...vantage_in_learning_from_experiments.ipynb | 208 ++++++++++++------ 1 file changed, 138 insertions(+), 70 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 492f76231..345bf1cb6 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -295,20 +295,30 @@ "outputs": [], "source": [ "rand_source = np.random.RandomState(20160913)\n", - "n_paulis = 200\n", + "n_paulis = 7\n", "n = 3\n", "n_shots = 47\n", "n_sweeps = 1\n", "\n", - "paulis = np.array([\"X\", \"Y\", \"Z\", \"I\"])\n", - "pauli_strings = rand_source.choice(a=paulis, size=(n_paulis, n), replace=True)\n", - "\n", "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", - "\n", "simulator = cirq.Simulator()\n", "\n", "all_results = []\n", - "for pauli in pauli_strings:\n", + "\n", + "for pauli_num in rand_source.choice(range(4 ** n), n_paulis, replace=False):\n", + " pauli = ''\n", + " for _ in range(n):\n", + " base4 = pauli_num % 4\n", + " if base4 == 0:\n", + " pauli += 'I'\n", + " elif base4 == 1:\n", + " pauli += 'X'\n", + " elif base4 == 2:\n", + " pauli += 'Y'\n", + " else:\n", + " pauli += 'Z'\n", + " pauli_num = (pauli_num - base4) // 4\n", + "\n", " circuit, sweeps = build_circuit(system_pairs, pauli, n_shots, rand_source)\n", " \n", " results_for_pauli = []\n", @@ -327,15 +337,13 @@ "\n", " batch_results = np.array(batch_results)\n", " results_for_pauli.append(batch_results)\n", - " all_results.append(np.concatenate(results_for_pauli))\n", - " " + " \n", + " all_results.append(np.concatenate(results_for_pauli))\n" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "We encode the results so that they can be ingested by our neural network." ] @@ -344,100 +352,160 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((400, 47, 12), (400, 2))" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "def _encode_pauli(paulis):\n", - " encoded = []\n", - " for pauli in paulis:\n", - " if pauli == 'I':\n", - " encoded.extend([0, 0])\n", - " elif pauli == 'X':\n", - " encoded.extend([0, 1])\n", - " elif pauli == 'Y':\n", - " encoded.extend([1, 0])\n", - " elif pauli == 'Z':\n", - " encoded.extend([1, 1])\n", - " return np.asarray([encoded])\n", - " \n", - "inputs = []\n", - "targets = []\n", - " \n", - "for i in range(n_paulis):\n", - " encoded_pauli = np.repeat(_encode_pauli(pauli_strings[i, :]), n_shots, axis=0)\n", + "class InnerLayer(tf.keras.Model):\n", + " def __init__(self, n_shots, n):\n", + " super(InnerLayer, self).__init__(name='')\n", + " self.n_shots = n_shots\n", + " self.n = n\n", + " self.gru1 = tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True)\n", + " self.gru2 = tf.keras.layers.GRU(4, go_backwards=True, return_sequences=True)\n", + " self.gru3 = tf.keras.layers.GRU(4, go_backwards=False, return_sequences=False)\n", + "\n", + " def call(self, x):\n", + " x = tf.expand_dims(tf.reshape(x, (-1, 2 * self.n)), -1)\n", + " x = self.gru1(x)\n", + " x = self.gru2(x)\n", + " x = self.gru3(x)\n", + " x = tf.reshape(x, (-1, self.n_shots, 4))\n", + " return x\n", + "\n", + "class IntermediateLayer(tf.keras.Model):\n", + " def __init__(self):\n", + " super(IntermediateLayer, self).__init__(name='')\n", " \n", - " inputs.append(np.expand_dims(np.concatenate((all_results[i], encoded_pauli,), axis=1), axis=0))\n", - " targets.append([1, 0])\n", + " def build(self, input_shape):\n", + " self.kernel = self.add_weight(\"kernel\", shape=[int(input_shape[2]), 2])\n", " \n", - " inputs.append(np.expand_dims(np.concatenate((all_results[(i + 1) % len(all_results)], encoded_pauli,), axis=1), axis=0))\n", - " targets.append([0, 1])\n", - "\n", - "inputs = np.concatenate(inputs)\n", - "targets = np.asarray(targets)\n", + " def call(self, x):\n", + " x = tf.math.reduce_mean(x, axis=1)\n", + " x = tf.matmul(x, self.kernel) \n", + " return x\n", "\n", - "inputs.shape, targets.shape\n" + "model = tf.keras.Sequential()\n", + "model.add(InnerLayer(n_shots, n))\n", + "model.add(IntermediateLayer())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We train the model" + "We define the conjoined model" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "model = tf.keras.Sequential()\n", - "model.add(tf.keras.layers.Dense(8, activation='relu', use_bias=True))\n", - "model.add(tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True))\n", - "model.add(tf.keras.layers.GRU(4, go_backwards=True, return_sequences=True))\n", - "model.add(tf.keras.layers.GRU(4, go_backwards=False))\n", - "model.add(tf.keras.layers.Dense(8, activation='relu', use_bias=True))\n", - "model.add(tf.keras.layers.Dense(2, activation='softmax', use_bias=True))\n", + "input_1 = tf.keras.Input((n_shots, 2 * n,))\n", + "input_2 = tf.keras.Input((n_shots, 2 * n,))\n", + "\n", + "encoded_1 = model(input_1)\n", + "encoded_2 = model(input_2)\n", + "\n", + "class OuterLayer(tf.keras.Model):\n", + " def __init__(self):\n", + " super(OuterLayer, self).__init__(name='')\n", + "\n", + " def call(self, x):\n", + " return tf.nn.softmax(x[0] - x[1]) \n", "\n", + "predictor = OuterLayer()\n", + "prediction = predictor([encoded_1, encoded_2])\n", + "\n", + "conjoined_net = tf.keras.Model(inputs=[input_1, input_2], outputs=prediction)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We train the model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "1/1 [==============================] - 6s 6s/step - loss: 0.6931 - accuracy: 0.7143\n", + "Epoch 2/10\n", + "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.7143\n", + "Epoch 3/10\n", + "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.7143\n", + "Epoch 4/10\n", + "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.7143\n", + "Epoch 5/10\n", + "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.7143\n", + "Epoch 6/10\n", + "1/1 [==============================] - 0s 23ms/step - loss: 0.6931 - accuracy: 0.7143\n", + "Epoch 7/10\n", + "1/1 [==============================] - 0s 22ms/step - loss: 0.6931 - accuracy: 0.7143\n", + "Epoch 8/10\n", + "1/1 [==============================] - 0s 22ms/step - loss: 0.6931 - accuracy: 0.7143\n", + "Epoch 9/10\n", + "1/1 [==============================] - 0s 25ms/step - loss: 0.6931 - accuracy: 0.7143\n", + "Epoch 10/10\n", + "1/1 [==============================] - 0s 25ms/step - loss: 0.6931 - accuracy: 0.7143\n" + ] + } + ], + "source": [ "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0005)\n", "loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n", "\n", - "model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n", + "conjoined_net.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n", + "\n", + "x1 = []\n", + "x2 = []\n", + "y = []\n", + "for i in range(n_paulis):\n", + " x1.append(all_results[i].astype(float))\n", + " x2.append(all_results[i].astype(float))\n", + " y.append([1.0, 0.0])\n", + " \n", + " x1.append(all_results[i].astype(float))\n", + " x2.append(all_results[(i + 1) % n_paulis].astype(float))\n", + " y.append([0.0, 1.0])\n", + " \n", + "x1 = np.stack(x1)\n", + "x2 = np.stack(x2)\n", + "y = np.stack(y)\n", + "\n", + " \n", + "loss = conjoined_net.train_on_batch((x1, x2), y)\n", "\n", - "history = model.fit(\n", - " x=inputs.astype(float),\n", - " y=targets.astype(float),\n", - " epochs=1000,\n", - " verbose=0)\n" + "history = conjoined_net.fit(\n", + " x=[x1, x2],\n", + " y=y,\n", + " epochs=10,\n", + " verbose=1)\n" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "Plot the results." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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KwxTj6QaIi4FrgW+r6magNXBhE9d0AVaEnq/00pKNEJEFIjJNRLqF0ncSkRoReVNETot6AxEZ7Z1Ts3r16jQ/isnYyJHR6an+4VoVU3myAJFdBRwgDgeWqOp6Efk+8HNgQxbe/xmgh6p+C5gN/C50bB9vEYuzgbtFZN/ki1V1sqpWq2p1x44ds5Adk5ZTToG6Oti8GcaODbrCbkjxT6KuLmdZMwXEAkR2NV6rH4t0A8RvgM0ichBwNfA+8PsmrlkFhEsEXb20r6jqGlX1h+k+CBwaOrbK2y4HXgEOTjOvJhdat4add4Zf/Qr693dp48ZFn7t1a86yZUzJKuASRL26tUmHA/eq6iRg1yaumQv0FpGeItIGGAUk9EYSkc6hp6cCi730KhFp6+13AAYByY3bplCcdJLb3ndfdHXSli2uWiob61yb4mEliOzyA8SOppp/syfdALFRRMbhurf+RURa4dohUlLVemAMMBN343/S6/00QURO9U77sYgsFJG3gR8DF3jpBwA1XvrLwK0RvZ9MoRgwADp0cPtHHglvvumCQivvn9eWLTBtWk77b5sCYAEiu/JQgki3m+uZuLaAi1T1UxHpDjT5v11VnwWeTUr7RWh/HNCgXkJV/w58M828mUKweDF07Ahz5sDhh7uJ/vx/yFu2JJ67fj3MmwfHH5/zbJocCt/Itm1z1ZKm5Qq1DUJVPwX+AOwuIsOAraraVBuEKScdOkCfPsHzRYuCG0RyG8R3v+sG323cmLv8mdwLB4j16/OWjZJTaG0QInIG8A9gJHAGMEdEvhdnxkwRevFFt91jj8T05EBQU+O2qXo9mdKzaVO+c1D8CriK6TrcGIjPAESkI/ACMC2ujJki1LVr8I83XBxeERoOc/LJQSPb+vXuGlOawjcya4/IXAH3YmrlBwfPmmZca8rRRx/BypVu//77g/Rnnw3+gVu1Q2kL38hy2POmZOWhDSLdEsTzIjITeNx7fiZJjc/GJOjePfWxzZvdNlWA2L7drYNtipuVIOJRaCUIVR0LTAa+5T0mq+o1cWbMlIjGurZGTY/y+ONuDexFoV7N778Ps2ZlP28md6wEkbkCboNAVf8I/DHGvJhSdOWV0Lata8BesgTefTc4dtFFcNhh0LOnGzPRpg385S/u2Jw5rqssuPUmwH6FFhurYsquQmuDEJGNIvJFxGOjiDRz5XpTlior4fLL4emng6nBw66/3k3ZMWiQe+7P/OpXQ/ntGOAWTFm7NtbsmiyyAJFdhVaCUNWmptMwJn39+7uR1qNGueeXXQZ/9AqlNTWuh5O/rsSXX7ptuGrptNPg2GPhpZdylWOTCWuDyK5CCxDGZFVFBbz6qttXdQEiLLzo0JIl8PbbwXQdvgUL4s2jyR4rQWRXoVUxGRMbEZg4MfXxKVNciSM5QNjU4cXJAkTmCribqzHZN2YMfPKJ2//lL6PPsQBRvKyKKR5WgjBlQQRuuQXOPjv1OckBorYWVoWWFdmxw1VbJf+nWbXKdZk1+WNVTNllVUymLB14oBvrEKW2tmFaeHqOGTPg6KPh7rsTzzn2WBd4bLGi/LEAkV0WIEzZ6tXLTd73hz8kliguuqjx6/wFip55JjH9o4/c1u82a3LPqpiyq1Cn+zYmJ3bbzQWH009P/xo/ACT/QvWfW5tFYbASRPZYCcKUte99L3pQXRS/CinVf5qoJVBNblgVU3b5f88CXHLUmNw65xz4978bP2fGjGDsRPJ/Gv8/U12dCzYPP5z1LJomWIDILv/vaSUIY4Bu3YL9hQvhmtD8kFu3wimnwJNPuufbtydeG65iOvdcuPDCePNqGrI2iOzKw9/QAoQpbPvv77Z9+yYGCH9SP9+OHTB2LPz2t+65/5/Jqpjyx0oQ2eX/DW2qDWM8f/0r/POfbpqOqip45x3XLfZ7SSvebt8Od9zh9s88M0i3RurCYAEic1bFZEySPfeEoUOD5337Rp/3WWjBw169gn0LEPljVUzZZQHCmCZUVsLzz7uG5+OOC9LDDdrhhYjCVUz33ht//kzAqpiyq9TaIERkqIgsEZFlInJtxPELRGS1iMz3HpeEjp0vIku9x/lx5tMUmSFDXC+nF1+ERx6BHj1SnxsuQVx+uQsY9ms2NyxAZFce2iBiCxAiUgFMAk4C+gFniUi/iFOfUNX+3uNB79r2wHhgIDAAGC8iVXHl1RSx738ffv3r1MeTG6nbtIGrr443T6YhCxCZK7EqpgHAMlVdrqp1wFRgeJrXDgFmq+paVV0HzAaGNnGNKVdHHQWdO0cfe/75hml33ZX6tTZtgmOOSVwa1bSMtUFkV4kFiC7AitDzlV5ashEiskBEpomI3/E9rWtFZLSI1IhIzepwvbMpL7vs4pYmHTKk4bH//d/mvdZLL7meU1bKyJxVMWVXKVUxpekZoIeqfgtXSvhdcy5W1cmqWq2q1R07dowlg6ZItGoFU6e6R+/e8OabLXuddu3c1l8T27ScBYjsKrGpNlYBoaGwdPXSvqKqa1TVn8/5QeDQdK81poE99nBjIN57DwYObPzcDz5w25dfTlz32ueviW1azqqYssv/GybPGhCjOAPEXKC3iPQUkTbAKGB6+AQRCVccnwos9vZnAoNFpMprnB7spRnTfAMGNEzzx0ocd5yrmrr9dhg0KCg5WIDILitBZM4PEDmcwj62kdSqWi8iY3A39gpgiqouFJEJQI2qTgd+LCKnAvXAWuAC79q1InITLsgATFDVtXHl1ZSoRYvcyOu994Yjjmh4PDy//s9+5rabNuUmb+XAqpiyy/8b5nD6mFin2lDVZ4Fnk9J+EdofB4xLce0UYEqc+TMl7oAD3EMVHnzQrYHd1Apzi71CbB4WZyk55VDFpJq7fyt5KEHku5HamPiJwMUXuzmcmvI//+O24bWwFy2C7t1h9ux48leqyqEEERX43nkH/vWv+N4rhyUICxCmfPjThzc2DsJXURHsv/surFgRVEOZ9JRrgPjmN+Fb34rvvawEYUwM7rwThg1z61zPmQNf+1rqc+fPD6qj/IbrZcvSf68HHgjWqjDlFSDi4v8NLUAYE4MePeCZZ9za1wMGwMknN37+DTe4rd+jqTndC0ePTpx2vByVSxtErt/LAoQxOfDrX8OECW4U9oYNsNdeicfnzXNbvwSxZYsLMCY9pVrFlK/AZ20QxuRQ+/Zw/fXQpYsrVcyaBTfeGBx/4QVX0vjJT4K0U091N7tvfxuefjrx9d5+2zWI+4Gl3FmAiOd9rQRhTB5861vwi1+4cRO+uXMbnnfUUVBT46Ycr6+HadPcf16/zeGRR3KT32JSSlVM+QoQeWiDsCVHjUn25puuS+wXX0Qff/31YH+PPVwbxXPPwfr1Lu2ee+LOYXGwEkQ872slCGPyqFu39BqYVYMGbNXUAcW3bp2rriqXZVBLNUCElXgbhJUgjIlyyy1uPeyNG2HixOhztmwJ9r/8sumbxbhxcP/9cMghbqGjphx/vGsgf+ON9PNdSEq1F5OVIIwpcx06uFHV99zjfrE11SV25Mimu8HWehMXp9td9qWXWj5teSFIVYKYNKnxMShx27QJzjoLPvusZdfnuw2iRKb7NqY0VFbCfffBFVfA4MGpzyumX/pvvQW/a9byK5kJ39TGjHElrhxOW53g4YfduiHhHmvNEQ4KftDPhRKb7tuY0tG1K9x9N/zyl+75a69Bv6Ql1j/6qPHXiPq1+cQT8POf5/5mecghcMEF8b5HqhKEP7ldvtpi/Hy1dJK98Odaty7z/DT3fXNYgrA2CGOa45BDgv+o//oXPPQQXHJJ6vPXr3c9nVIZNcptW7eG8eOjz8nljKHZlKoqplUrFxC3bYOdd859vnzZCBBr1sB++2UnP+m+r1UxGVMEWrVys8TOmAE//nH0Oa++6gbcQfAffPJkeOyxxPPmzEn9Pscfn3le8yF8Iw2XkPyJEEuhBLE2h8vU5KENwkoQxmTq5JNh//1db6e994aPPw6ODR/uttNDiyn+/e/use++QVqbNqlf/+WXs5vfXEkVIPJdxeTfYIu1iimHDeNWgjAmG/bd11WZrFoVXeUwYQL8/veJaYcdFuz7AWLLFjjvPDc/VCnZtg2ef94FUX+tjRz258+q8A06h11O8zGuxAKEMdlS6RXIr7/ebS+8MDhWU5PetS+84KbqGDMmOHb44W67eXNxjScI53XbNjjpJNcTrJSqmHIZIMJBIUedGixAGJNt554Lf/ub6++fLv+m07at237+eXBsxw5XlbHLLnDzzdnLp//acUkOED6/BGEBouXvawHCmCIlAoMGNa+HTseObrtpk9uGB3HV1rreMuDW1m7M00+79093cSP/RvPXv8K//+32n3kGFixI7/rGxBEgFi50JZDlyzPPVzYCRC67JycHpueeiz3IWoAwJk6XXQZHHgmPPgqnnZb6PH/1ug0b3Hbp0uBYbS38+c9uv67O3ZTCEwaGPfqo2/7zn+nlz/8FfMwx0Lu32z/1VDjooPSub0z4hhb+PJm0Qfz2t67U86c/ZZY3yE7X4XyVIF55Bb7zHTeGJkYWIIyJ0733uq6u55zjbmo33+zq4ZM98IC7YUUtSFRXBz/9qduvrXVTgBxxhOsJlay5PXTCv4Dj/DX6f/8X7GdSgvDz2yqDW1exVjGFqwNXrXLbTEpSabAAYUwu/b//50Zk/+c/wSC5sORfxrvtljidQ22tG6AHsGJFw+v9m1e6N9A4q0hSNahnI0BUZtBDP9NuruG/WS6rmMIBwv/bZRIo0xDrq4vIUBFZIiLLROTaRs4bISIqItXe8x4iskVE5nuP++LMpzE5t+eecOutrkvrtGmpz+vQIbHLa21t0AsoqoH5L39JfSxKfX18PaPiCBD+L3b/b5CJlgaI8N82XyWIHAWI2AbKiUgFMAk4EVgJzBWR6aq6KOm8XYErgOShpO+rav+48mdM3u2zTzBh3iefQOfODc9J/qVcXx+sXBf169Wv1/fXqQA3PUibNsHMsOH2gO3b47vJxVmCyCRAZFrFVEYBIs5XHwAsU9XlqloHTAWGR5x3E3AbsDXGvBhT2Dp1gnffDZ77EwG+917qa2prYfHioEts+IYcLnW89VbiVB7hqqn6+vx1N21J9UyhBYh8VTH5PwSyUZJqRJwBogsQriRd6aV9RUQOAbqp6l8iru8pIm+JyF9F5MioNxCR0SJSIyI1q1evzlrGjcmLvn1h9mw3U+xrr8HjjwelhSiXXOICiR9Mwjd6f7BelPCv3u3bE6/LZnVTU6+V7wDRUlaCiJ+ItALuBK6OOPwJ0F1VDwZ+AjwmIrsln6Sqk1W1WlWrO/r9yI0pZiec4HootW/vGrFHjnTdZAGGDIm+xv9xtHlzeu8Rrn5KLkEsWdL8PKeS6kbc2MpoN93kftmnujYbjdSlUMXkd1wo4gCxCugWet7VS/PtChwIvCIiHwKHAdNFpFpVa1V1DYCqzgPeB/rEmFdjCtcDD8BVV8FTT6VeYe699xK7kvbokfr1wgHCn3bb99ZbGWU1QaqbvH+jiypB+FOepxojkY1Gaj9ft97asusLoYrJ/w6LOEDMBXqLSE8RaQOMAr6a0lJVN6hqB1Xtoao9gDeBU1W1RkQ6eo3ciEgvoDcQb4dfYwpV375w553Qrp1rcE51zg9+4PZ32SUYkR0OBr7GShBPPZWdPEPTpYCoX99NzfRaaFVMt9+e2Wu19H3977dY2yBUtR4YA8wEFgNPqupCEZkgIqc2cflRwAIRmQ9MA36oqjmceN2YAtW6tZtu4rXXUp8jEgSB/fdveDxcFZXcBjF3brAfV/dX/yYf9evb/0WcaqoQ/5pM5pDKZoAIq69Pv5ov0/fdssVtY568Mdbyiao+q6p9VHVfVb3ZS/uFqk6POPcYVa3x9v+oqt9Q1f6qeoiqRgwvNaZM9evn2imuvtoNukv+FVlX524gxx6b2Jtp6VIYNgw+/TRICweINm1g48bgmF8iaSn/5uUv0+rzSw5RJQj/sxx8cPRrNhZc0pXpBIWprj/lFFd6i0tUgIh52m8bSW1MsbrjDjdthz/dwte+5rYDB7rtK68knt+njxtI96tfBWnhKqbdd09cIe2BBzLLnx8gkhdD8gND1E2+qYbjbJQgMm1YTn5v//nzz2f2uk0J/738QB5zG4gFCGOKXffu7ma8caPb3nhj+tdu3x70iGls7eyW8ANE8gBAvwE66kbdVKNrOLhs3dqyMRyZjvtIvinnqidTODD5ASLm97YAYUypOfZYtx7FXXc1fe5nn8EHH7j9vn2zmw8/QHTp4pZl9TVWxRQOEFH16+Eqpp13hv79m5+v8NxWLZFcgshHgPjiC7f9wx9Sz+ybBRYgjClFgwbBlVe6X+tRs77uuaebCPCaa9yAvIoKdyNPtm6dmzr8mWdgypSW5UUE9tqrYXpTVUxRv/ST2yAWL25+fsIBoiWNvPkMEP74j3BbUYxVW7HNxWSMKQCVlW7J0rFjXZfMp55yCwP17Olu/Dfe6HpFtWnjgkGy9u0Tn48Y4doqwjZscLPQXnBBYnr45pv8OtB0CaKuLlhhz5eNRupwgNixo/ldRZMDRK7W1t6xA3bd1X1P4QAR41gICxDGlIPbbnM9mI48MviVXlUVtFfU1SWuYpfKZ581DBDnn+8WNKquhgMPDNL9xYtEggb0sKZu8rW17oYYdU0mjdThAFFfn3mAyGUJ4mtfcwHC78UEsY6FsComY8qBCBx1VGIVzre/nThlxdixTb9OeJDdypVuqVR/tbtk/ky1ItE3sagbqz8ADKLbCrJdgmjJzd0PEKd6w7lyWYLYeeeGJYZsrIyXggUIY8rVTjsFv0TPOsstYTlnDowZ4yYJ7BMxu004QDz9dDCTLKRu/E01t1LUTT6cVugBwu++m+o1Mh3E9sQTrodauNRUUeEGS4ZZFZMxJhaVlW70r3+zGzDAPQDmzXNVU2HhAJFcKghXe6TTlTTqxtqpUzCQL64AEc5bcwLEvHmuDWfYMPfcbx9pLEBk8ut+9GjXW2njRtcFeccOFwxat07821gJwhgTm513jq4Ciup5FJ4QsLEAEa4qak4J4hvfCPYLrQQxcCDccktQpeQH1VRVTI21k4wdGz0NShQ/j36ASJ7J1koQxpicu+giV8Vx883uV/0nn8CDD7q69zfegK9/PfH8cM+a8L4IHHNMw9ePujmH09INEM39pR5+3eYEGv9c//p0ShCp3HFH+u+71VtLLVyCCIuxBGEBwhgTbffdXbfWESPcKnTdu7t0v3E2eZDaiBHBTTG8ap3fQL5pE3TrFnSnjbo519e7tpGtW4PBYOB+pZ9xBrzzjnsevslv29ZwOo/GZNoG4V/fVBtEpvMk+Td+//1SBYgYSxBWxWSMaVrXrjBxYuJgufnzG54n4uZ68hc58tPATWQXHq19zz0wa1bi9du2uXYISGwAX7TINYr7wtVZzZ1BNdMA4f+iz6SKKR3+3y25BJFcxWRtEMaYvBKByy+HCy90U3n4dtqp4bnXXJP4PDzDaYcOiceuuCLx+ebNwYjucIBI/tW8dWviNc2RrQCRSRVTOuekW4KwAGGMKRizZrlfzTt2wJo1cOmlLnAcf3z0+d1CC0u2a5d47NNPYfp0OOwwGDrUVSH16uVueuEAEQ4I0HgJ4s473fXhdpCw2togH5lUMfkBIpMSRDrvX1vr/i7z5kUHiBgH6lmAMMY0T2Wle4i4G+3kya7q6YUXolek2y20nLw/0nrqVDjxRFi/HoYPd+MvZs50x0aMcKO8p02Dl15qOHIYEieoS14179573Ta87kVYbW1QqmlJb6jkNohMAkQ6Ewdu3Qrf/KYbxZ7jAGGN1MaY7Dn9dFi92q1M17FjYmM1uOqnkSNdF88+fWD27IavMXy4q4patCgolfjBw/fuu8F+cgnC736b6uZbW+sCEGRWgth5Z7dNNeYjnbEgtbXR05BAwyomSGyD6NzZ9SyLcSS3lSCMMdnVoQOcdJKbm+n00xOPtWkT9P/v3x9uuslV1SQ3Vq9fn/g8HBCShcdcQBAghg1zwejQQxOP19UFczwlV12lw7/GL4Ukl258UeuBJ0u3BOELlyD22MM9txKEMabkiMDPf+4e4GaD9dsGkicOrKlJ/TqLFrnqKp/f7fOjj9wjTNXdlDt1cvX6yYEoHf706eEAEfUrPtMAkaoE8Y9/uP0NG1ywsBKEMabkPfQQTJrk9ufMSTz2yCNue++9QRsDuB5PyedGjQp/+223ra93QcJf5W7NmsTz1qyB115rPJ8zZritHyA2b44OBqkCRLjnUjoliOQA4fv4Y1fdZCUIY0xZGTDADcz7978T0887z1UPbdniqqtee80tePSnP7lSR7t2DUd4g6vO8ksPEASItWtd+vnnw3e/66Y/nz/frbLXo0fjeQyXIJKruSB1gAj/4m9JFVNyHqLeO0ssQBhjCtNtt8Ell7iG2MMOcw3WftvBT3/qtsOGud5OW7cm9pbq1AnOPtt1efW9914QPDp1clU48+cHN93nn3cN7OAWVGpqHIMfIBYtghNOCNL339+1maQKEOFG9cZu7n4V0w9+EKQlB4g990xvHY8WsiomY0xhGjXK3UB33dWtejdxYsNzevVKrHLyHXdcw6lA+vYNBuq1a+cm33vooeC4HxySzZ3rSijJ/PaSyZOD6UdGjgyqw/wAMWFCYt7DASJqFb/GRAWI//ynea/RnLeL7ZWNMSYXLrvM1cOvXx80IB9/vCt1fPOb7gadrKoKfvKTxl/39dfdDXzAAFf9lCw85cXy5W57222wzz5uf9Uqtx0/PnHE+DPPBPuNNZJHjZBu1Sox2HTq5EpYMYk1QIjIUBFZIiLLROTaRs4bISIqItWhtHHedUtEZEic+TTGFLmKCje54OGHu1/uF14IvXvDggVw/fXwyiuJ80OddJJbICk8vXiyI46Ibs/wtWrV8Pg++7jxH3vt5d47XE21caMbHf7DHwZpjZUgogbx7dgBF18cPN9vP9dO05LuummILUCISAUwCTgJ6AecJSL9Is7bFbgCmBNK6weMAr4BDAV+7b2eMcY0rl27hr++jz4aXn3VrdL20EOu2mqXXVxXV1XXjvDSS+6mHJ4yJLkdwq8+AheU3n4bXnzRPRcJqoB69ICHH3bVUz4/eIStXeu2S5c2nBokaqDdtm2J05X07euCxvvvR/0lMhZnCWIAsExVl6tqHTAVGB5x3k3AbUA4BA4Hpqpqrap+ACzzXs8YY1rujDPceItkBxzgJiFs1cpNGVJX55ZhBfcrHdxSrN//vpu0ENxAtS5dYNAg97pvvhm83tFHu+3AgUFacmmhUyf48EMXhPr0cdc0tR5G8piHvfd221TTimQozgDRBQiPs1/ppX1FRA4BuqnqX5p7rXf9aBGpEZGa1akamIwxprlat4bHHnM37/fec4PS7rnHHZs40f3a9xu827Z1JZMBod+w48e7JUMBhkTUkE+Z4qrAXnzRBR2At94KXnPHjsRg4PeY8ktGIq4ay1/1L6aG6rw1UotIK+BO4OqWvoaqTlbValWt7phcdDPGmGwQcV1owz2IUs2f5GvXDu67z5Uqnn0WrroqmMYcXBvJ6ae7uaoeeyxI37DBrTaXXL00bZrb+hMAfvmlK334AaIISxCrgNA8v3T10ny7AgcCr4jIh8BhwHSvobqpa40xprCJuCqmVq3ceIyPPnK9rP72N3d8zJjgXH98B7j1qm+4we0fdJBrYPfbHfxR4jvv7NbiqKpyg/6SR5NnSZwD5eYCvUWkJ+7mPgo42z+oqhuAr1YPEZFXgJ+qao2IbAEeE5E7gb2B3sA/YsyrMcbEq6LC9bLytW4NS5a47d57ux5Oxx3nqrRuu82dc911rm2irg7OPTeYt8on4to/Uq19kaHYAoSq1ovIGGAmUAFMUdWFIjIBqFHV6Y1cu1BEngQWAfXAZaragonbjTGmgPXpE+x36eICxssvu9lt/a664KYV+f3vo1/j7rtjy55oOsviFYHq6mqtaWzGR2OMMQ2IyDxVrY46ZiOpjTHGRLIAYYwxJpIFCGOMMZEsQBhjjIlkAcIYY0wkCxDGGGMiWYAwxhgTyQKEMcaYSCUzUE5EVgMfZfASHYDPs5SdYmGfufSV2+cF+8zNtY+qRs52WjIBIlMiUpNqNGGpss9c+srt84J95myyKiZjjDGRLEAYY4yJZAEiMDnfGcgD+8ylr9w+L9hnzhprgzDGGBPJShDGGGMiWYAwxhgTqewDhIgMFZElIrJMRK7Nd36yRUS6icjLIrJIRBaKyBVeensRmS0iS71tlZcuIjLR+zssEJFD8vsJWk5EKkTkLRGZ4T3vKSJzvM/2hIi08dLbes+Xecd75DXjLSQie4jINBF5V0QWi8jhpf49i8hV3r/rd0TkcRHZqdS+ZxGZIiKficg7obRmf68icr53/lIROb85eSjrACEiFcAk4CSgH3CWiPTLb66yph64WlX7AYcBl3mf7VrgRVXtDbzoPQf3N+jtPUYDv8l9lrPmCmBx6PltwF2quh+wDrjYS78YWOel3+WdV4zuAZ5X1f2Bg3CfvWS/ZxHpAvwYqFbVA3FLGo+i9L7nh4GhSWnN+l5FpD0wHhgIDADG+0ElLapatg/gcGBm6Pk4YFy+8xXTZ/0zcCKwBOjspXUGlnj79wNnhc7/6rxiegBdvf84xwEzAMGNMK1M/s5x66Uf7u1XeudJvj9DMz/v7sAHyfku5e8Z6AKsANp739sMYEgpfs9AD+Cdln6vwFnA/aH0hPOaepR1CYLgH5pvpZdWUrwi9cHAHGAvVf3EO/QpsJe3Xyp/i7uBnwE7vOdfB9arar33PPy5vvrM3vEN3vnFpCewGnjIq1Z7UER2oYS/Z1VdBdwB/Bv4BPe9zaO0v2dfc7/XjL7vcg8QJU9Evgb8EbhSVb8IH1P3k6Jk+jmLyDDgM1Wdl++85FAlcAjwG1U9GPiSoNoBKMnvuQoYjguOewO70LAqpuTl4nst9wCxCugWet7VSysJItIaFxz+oKpPecn/EZHO3vHOwGdeein8LQYBp4rIh8BUXDXTPcAeIlLpnRP+XF99Zu/47sCaXGY4C1YCK1V1jvd8Gi5glPL3fALwgaquVtVtwFO4776Uv2dfc7/XjL7vcg8Qc4HeXu+HNriGrul5zlNWiIgAvwUWq+qdoUPTAb8nw/m4tgk//TyvN8RhwIZQUbYoqOo4Ve2qqj1w3+VLqnoO8DLwPe+05M/s/y2+551fVL+0VfVTYIWI9PWSjgcWUcLfM65q6TARaef9O/c/c8l+zyHN/V5nAoNFpMoreQ320tKT70aYfD+A7wDvAe8D1+U7P1n8XEfgip8LgPne4zu4utcXgaXAC0B773zB9eh6H/gXrodI3j9HBp//GGCGt98L+AewDPg/oK2XvpP3fJl3vFe+893Cz9ofqPG+66eBqlL/noEbgXeBd4BHgLal9j0Dj+PaWLbhSooXt+R7BS7yPvsy4MLm5MGm2jDGGBOp3KuYjDHGpGABwhhjTCQLEMYYYyJZgDDGGBPJAoQxxphIFiCMMcZEsgBhjDEm0v8HogZ0KvdqnpYAAAAASUVORK5CYII=\n", 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\n", 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" ] From 0ba96fc294ff22bd953e49467aad4c59c66df2a0 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 2 Jan 2022 23:32:36 +0000 Subject: [PATCH 13/27] No more NaN --- ...vantage_in_learning_from_experiments.ipynb | 264 +++++++++++++----- 1 file changed, 199 insertions(+), 65 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 345bf1cb6..aedd71180 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 25, "metadata": { "colab": {}, "colab_type": "code", @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -290,15 +290,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "rand_source = np.random.RandomState(20160913)\n", - "n_paulis = 7\n", + "n_paulis = 2\n", "n = 3\n", - "n_shots = 47\n", - "n_sweeps = 1\n", + "n_shots = 11\n", + "n_repeats = 2\n", "\n", "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", "simulator = cirq.Simulator()\n", @@ -322,23 +322,26 @@ " circuit, sweeps = build_circuit(system_pairs, pauli, n_shots, rand_source)\n", " \n", " results_for_pauli = []\n", - " for b in range(0, n_shots, n_sweeps):\n", - " results = simulator.run_sweep(\n", - " program=circuit,\n", - " params=sweeps[b : b + n_sweeps],\n", - " repetitions=1\n", - " )\n", + " for _ in range(n_repeats):\n", + " results_for_repeat = []\n", + " for b in range(n_shots):\n", + " results = simulator.run_sweep(\n", + " program=circuit,\n", + " params=sweeps[b : b + n_sweeps],\n", + " repetitions=1\n", + " )\n", "\n", - " batch_results = []\n", - " for j, single_circuit_samples in enumerate(results):\n", - " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", - " out0 = single_circuit_samples.data[qubit_order].to_numpy()\n", - " batch_results.append(np.squeeze(out0))\n", + " batch_results = []\n", + " for j, single_circuit_samples in enumerate(results):\n", + " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", + " out0 = single_circuit_samples.data[qubit_order].to_numpy()\n", + " batch_results.append(np.squeeze(out0))\n", "\n", - " batch_results = np.array(batch_results)\n", - " results_for_pauli.append(batch_results)\n", - " \n", - " all_results.append(np.concatenate(results_for_pauli))\n" + " batch_results = np.array(batch_results)\n", + " results_for_repeat.append(batch_results)\n", + "\n", + " results_for_pauli.append(np.concatenate(results_for_repeat))\n", + " all_results.append(results_for_pauli)\n" ] }, { @@ -350,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -376,7 +379,7 @@ " super(IntermediateLayer, self).__init__(name='')\n", " \n", " def build(self, input_shape):\n", - " self.kernel = self.add_weight(\"kernel\", shape=[int(input_shape[2]), 2])\n", + " self.kernel = self.add_weight(\"kernel\", shape=[int(input_shape[2]), 8])\n", " \n", " def call(self, x):\n", " x = tf.math.reduce_mean(x, axis=1)\n", @@ -397,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -410,9 +413,12 @@ "class OuterLayer(tf.keras.Model):\n", " def __init__(self):\n", " super(OuterLayer, self).__init__(name='')\n", - "\n", + " \n", " def call(self, x):\n", - " return tf.nn.softmax(x[0] - x[1]) \n", + " x = tf.norm(x[1] - x[0], ord=2, axis=1)\n", + " x = tf.stack([x, tf.ones(tf.shape(x))], axis=1)\n", + " x = tf.nn.softmax(x)\n", + " return x\n", "\n", "predictor = OuterLayer()\n", "prediction = predictor([encoded_1, encoded_2])\n", @@ -429,66 +435,158 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10\n", - "1/1 [==============================] - 6s 6s/step - loss: 0.6931 - accuracy: 0.7143\n", - "Epoch 2/10\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.7143\n", - "Epoch 3/10\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.7143\n", - "Epoch 4/10\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.7143\n", - "Epoch 5/10\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.7143\n", - "Epoch 6/10\n", - "1/1 [==============================] - 0s 23ms/step - loss: 0.6931 - accuracy: 0.7143\n", - "Epoch 7/10\n", - "1/1 [==============================] - 0s 22ms/step - loss: 0.6931 - accuracy: 0.7143\n", - "Epoch 8/10\n", - "1/1 [==============================] - 0s 22ms/step - loss: 0.6931 - accuracy: 0.7143\n", - "Epoch 9/10\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.6931 - accuracy: 0.7143\n", - "Epoch 10/10\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.6931 - accuracy: 0.7143\n" + "Epoch 1/50\n", + "2/2 - 7s - loss: 0.8128 - accuracy: 0.5000\n", + "Epoch 2/50\n", + "2/2 - 0s - loss: 0.8127 - accuracy: 0.5000\n", + "Epoch 3/50\n", + "2/2 - 0s - loss: 0.8126 - accuracy: 0.5000\n", + "Epoch 4/50\n", + "2/2 - 0s - loss: 0.8124 - accuracy: 0.5000\n", + "Epoch 5/50\n", + "2/2 - 0s - loss: 0.8124 - accuracy: 0.5000\n", + "Epoch 6/50\n", + "2/2 - 0s - loss: 0.8122 - accuracy: 0.5000\n", + "Epoch 7/50\n", + "2/2 - 0s - loss: 0.8121 - accuracy: 0.5000\n", + "Epoch 8/50\n", + "2/2 - 0s - loss: 0.8120 - accuracy: 0.5000\n", + "Epoch 9/50\n", + "2/2 - 0s - loss: 0.8119 - accuracy: 0.5000\n", + "Epoch 10/50\n", + "2/2 - 0s - loss: 0.8118 - accuracy: 0.5000\n", + "Epoch 11/50\n", + "2/2 - 0s - loss: 0.8117 - accuracy: 0.5000\n", + "Epoch 12/50\n", + "2/2 - 0s - loss: 0.8116 - accuracy: 0.5000\n", + "Epoch 13/50\n", + "2/2 - 0s - loss: 0.8115 - accuracy: 0.5000\n", + "Epoch 14/50\n", + "2/2 - 0s - loss: 0.8113 - accuracy: 0.5000\n", + "Epoch 15/50\n", + "2/2 - 0s - loss: 0.8112 - accuracy: 0.5000\n", + "Epoch 16/50\n", + "2/2 - 0s - loss: 0.8111 - accuracy: 0.5000\n", + "Epoch 17/50\n", + "2/2 - 0s - loss: 0.8110 - accuracy: 0.5000\n", + "Epoch 18/50\n", + "2/2 - 0s - loss: 0.8108 - accuracy: 0.5000\n", + "Epoch 19/50\n", + "2/2 - 0s - loss: 0.8107 - accuracy: 0.5000\n", + "Epoch 20/50\n", + "2/2 - 0s - loss: 0.8105 - accuracy: 0.5000\n", + "Epoch 21/50\n", + "2/2 - 0s - loss: 0.8104 - accuracy: 0.5000\n", + "Epoch 22/50\n", + "2/2 - 0s - loss: 0.8102 - accuracy: 0.5000\n", + "Epoch 23/50\n", + "2/2 - 0s - loss: 0.8101 - accuracy: 0.5000\n", + "Epoch 24/50\n", + "2/2 - 0s - loss: 0.8099 - accuracy: 0.5000\n", + "Epoch 25/50\n", + "2/2 - 0s - loss: 0.8097 - accuracy: 0.5000\n", + "Epoch 26/50\n", + "2/2 - 0s - loss: 0.8096 - accuracy: 0.5000\n", + "Epoch 27/50\n", + "2/2 - 0s - loss: 0.8093 - accuracy: 0.5000\n", + "Epoch 28/50\n", + "2/2 - 0s - loss: 0.8092 - accuracy: 0.5000\n", + "Epoch 29/50\n", + "2/2 - 0s - loss: 0.8090 - accuracy: 0.5000\n", + "Epoch 30/50\n", + "2/2 - 0s - loss: 0.8088 - accuracy: 0.5000\n", + "Epoch 31/50\n", + "2/2 - 0s - loss: 0.8086 - accuracy: 0.5000\n", + "Epoch 32/50\n", + "2/2 - 0s - loss: 0.8083 - accuracy: 0.5000\n", + "Epoch 33/50\n", + "2/2 - 0s - loss: 0.8081 - accuracy: 0.5000\n", + "Epoch 34/50\n", + "2/2 - 0s - loss: 0.8079 - accuracy: 0.5000\n", + "Epoch 35/50\n", + "2/2 - 0s - loss: 0.8075 - accuracy: 0.5000\n", + "Epoch 36/50\n", + "2/2 - 0s - loss: 0.8073 - accuracy: 0.5000\n", + "Epoch 37/50\n", + "2/2 - 0s - loss: 0.8072 - accuracy: 0.5000\n", + "Epoch 38/50\n", + "2/2 - 0s - loss: 0.8068 - accuracy: 0.5000\n", + "Epoch 39/50\n", + "2/2 - 0s - loss: 0.8064 - accuracy: 0.5000\n", + "Epoch 40/50\n", + "2/2 - 0s - loss: 0.8061 - accuracy: 0.5000\n", + "Epoch 41/50\n", + "2/2 - 0s - loss: 0.8058 - accuracy: 0.5000\n", + "Epoch 42/50\n", + "2/2 - 0s - loss: 0.8054 - accuracy: 0.5000\n", + "Epoch 43/50\n", + "2/2 - 0s - loss: 0.8050 - accuracy: 0.5000\n", + "Epoch 44/50\n", + "2/2 - 0s - loss: 0.8046 - accuracy: 0.5000\n", + "Epoch 45/50\n", + "2/2 - 0s - loss: 0.8041 - accuracy: 0.5000\n", + "Epoch 46/50\n", + "2/2 - 0s - loss: 0.8037 - accuracy: 0.5000\n", + "Epoch 47/50\n", + "2/2 - 0s - loss: 0.8034 - accuracy: 0.5000\n", + "Epoch 48/50\n", + "2/2 - 0s - loss: 0.8029 - accuracy: 0.5000\n", + "Epoch 49/50\n", + "2/2 - 0s - loss: 0.8025 - accuracy: 0.5000\n", + "Epoch 50/50\n", + "2/2 - 0s - loss: 0.8019 - accuracy: 0.5000\n" ] } ], "source": [ - "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0005)\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n", "loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n", "\n", "conjoined_net.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n", "\n", + "def _sample_different(max_val, ref):\n", + " ret = ref\n", + " while ret == ref:\n", + " ret = rand_source.choice(max_val)\n", + " return ret\n", + "\n", "x1 = []\n", "x2 = []\n", "y = []\n", - "for i in range(n_paulis):\n", - " x1.append(all_results[i].astype(float))\n", - " x2.append(all_results[i].astype(float))\n", - " y.append([1.0, 0.0])\n", + "for pauli_idx in range(n_paulis):\n", + " # Same Pauli\n", + " for i in range(n_repeats):\n", + " j = _sample_different(n_repeats, i)\n", " \n", - " x1.append(all_results[i].astype(float))\n", - " x2.append(all_results[(i + 1) % n_paulis].astype(float))\n", - " y.append([0.0, 1.0])\n", + " x1.append(all_results[pauli_idx][i].astype(float))\n", + " x2.append(all_results[pauli_idx][j].astype(float))\n", + " y.append([1.0, 0.0])\n", + " \n", + " # Different Pauli\n", + " for i in range(n_repeats):\n", + " other_pauli_idx = _sample_different(n_paulis, pauli_idx)\n", + " j = rand_source.choice(n_repeats)\n", + " x1.append(all_results[pauli_idx][i].astype(float))\n", + " x2.append(all_results[other_pauli_idx][j].astype(float))\n", + " y.append([0.0, 1.0])\n", " \n", "x1 = np.stack(x1)\n", "x2 = np.stack(x2)\n", "y = np.stack(y)\n", "\n", - " \n", - "loss = conjoined_net.train_on_batch((x1, x2), y)\n", - "\n", "history = conjoined_net.fit(\n", " x=[x1, x2],\n", " y=y,\n", - " epochs=10,\n", - " verbose=1)\n" + " epochs=50,\n", + " batch_size=(2*n_paulis),\n", + " verbose=2)\n" ] }, { @@ -500,12 +598,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 73, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -526,6 +624,39 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[0.28876415 0.71123594]\n", + " [0.28876415 0.71123594]\n", + " [0.276356 0.723644 ]\n", + " [0.28192133 0.7180787 ]\n", + " [0.27187255 0.7281275 ]\n", + " [0.27187255 0.7281275 ]\n", + " [0.28490922 0.71509075]\n", + " [0.28192133 0.7180787 ]], shape=(8, 2), dtype=float32)\n", + "Model: \"model_11\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_25 (InputLayer) [(None, 11, 6)] 0 \n", + "__________________________________________________________________________________________________\n", + "input_26 (InputLayer) [(None, 11, 6)] 0 \n", + "__________________________________________________________________________________________________\n", + "sequential_12 (Sequential) (None, 8) 356 input_25[0][0] \n", + " input_26[0][0] \n", + "__________________________________________________________________________________________________\n", + "outer_layer_11 (OuterLayer) (None, 2) 0 sequential_12[0][0] \n", + " sequential_12[1][0] \n", + "==================================================================================================\n", + "Total params: 356\n", + "Trainable params: 356\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] } ], "source": [ @@ -535,7 +666,10 @@ "\n", "plt.plot(history.history['loss'], 'r')\n", "plt.ylabel('loss')\n", - "plt.show()\n" + "plt.show()\n", + "\n", + "print(conjoined_net((x1, x2)))\n", + "conjoined_net.summary()" ] } ], From 6064c32a5f2fb9206b48db4d16cf7d58f6183156 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Mon, 3 Jan 2022 02:52:52 +0000 Subject: [PATCH 14/27] Now converges --- ...vantage_in_learning_from_experiments.ipynb | 201 +++--------------- 1 file changed, 27 insertions(+), 174 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index aedd71180..12f15dd81 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -290,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -324,24 +324,20 @@ " results_for_pauli = []\n", " for _ in range(n_repeats):\n", " results_for_repeat = []\n", - " for b in range(n_shots):\n", - " results = simulator.run_sweep(\n", - " program=circuit,\n", - " params=sweeps[b : b + n_sweeps],\n", - " repetitions=1\n", - " )\n", - "\n", - " batch_results = []\n", - " for j, single_circuit_samples in enumerate(results):\n", - " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", - " out0 = single_circuit_samples.data[qubit_order].to_numpy()\n", - " batch_results.append(np.squeeze(out0))\n", + " results = simulator.run_sweep(\n", + " program=circuit,\n", + " params=sweeps,\n", + " repetitions=1\n", + " )\n", "\n", - " batch_results = np.array(batch_results)\n", - " results_for_repeat.append(batch_results)\n", + " batch_results = []\n", + " for j, single_circuit_samples in enumerate(results):\n", + " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", + " out0 = single_circuit_samples.data[qubit_order].to_numpy()\n", + " batch_results.append(np.squeeze(out0))\n", "\n", - " results_for_pauli.append(np.concatenate(results_for_repeat))\n", - " all_results.append(results_for_pauli)\n" + " results_for_pauli.append(np.array(batch_results))\n", + " all_results.append(results_for_pauli)" ] }, { @@ -353,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -400,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -435,118 +431,11 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/50\n", - "2/2 - 7s - loss: 0.8128 - accuracy: 0.5000\n", - "Epoch 2/50\n", - "2/2 - 0s - loss: 0.8127 - accuracy: 0.5000\n", - "Epoch 3/50\n", - "2/2 - 0s - loss: 0.8126 - accuracy: 0.5000\n", - "Epoch 4/50\n", - "2/2 - 0s - loss: 0.8124 - accuracy: 0.5000\n", - "Epoch 5/50\n", - "2/2 - 0s - loss: 0.8124 - accuracy: 0.5000\n", - "Epoch 6/50\n", - "2/2 - 0s - loss: 0.8122 - accuracy: 0.5000\n", - "Epoch 7/50\n", - "2/2 - 0s - loss: 0.8121 - accuracy: 0.5000\n", - "Epoch 8/50\n", - "2/2 - 0s - loss: 0.8120 - accuracy: 0.5000\n", - "Epoch 9/50\n", - "2/2 - 0s - loss: 0.8119 - accuracy: 0.5000\n", - "Epoch 10/50\n", - "2/2 - 0s - loss: 0.8118 - accuracy: 0.5000\n", - "Epoch 11/50\n", - "2/2 - 0s - loss: 0.8117 - accuracy: 0.5000\n", - "Epoch 12/50\n", - "2/2 - 0s - loss: 0.8116 - accuracy: 0.5000\n", - "Epoch 13/50\n", - "2/2 - 0s - loss: 0.8115 - accuracy: 0.5000\n", - "Epoch 14/50\n", - "2/2 - 0s - loss: 0.8113 - accuracy: 0.5000\n", - "Epoch 15/50\n", - "2/2 - 0s - loss: 0.8112 - accuracy: 0.5000\n", - "Epoch 16/50\n", - "2/2 - 0s - loss: 0.8111 - accuracy: 0.5000\n", - "Epoch 17/50\n", - "2/2 - 0s - loss: 0.8110 - accuracy: 0.5000\n", - "Epoch 18/50\n", - "2/2 - 0s - loss: 0.8108 - accuracy: 0.5000\n", - "Epoch 19/50\n", - "2/2 - 0s - loss: 0.8107 - accuracy: 0.5000\n", - "Epoch 20/50\n", - "2/2 - 0s - loss: 0.8105 - accuracy: 0.5000\n", - "Epoch 21/50\n", - "2/2 - 0s - loss: 0.8104 - accuracy: 0.5000\n", - "Epoch 22/50\n", - "2/2 - 0s - loss: 0.8102 - accuracy: 0.5000\n", - "Epoch 23/50\n", - "2/2 - 0s - loss: 0.8101 - accuracy: 0.5000\n", - "Epoch 24/50\n", - "2/2 - 0s - loss: 0.8099 - accuracy: 0.5000\n", - "Epoch 25/50\n", - "2/2 - 0s - loss: 0.8097 - accuracy: 0.5000\n", - "Epoch 26/50\n", - "2/2 - 0s - loss: 0.8096 - accuracy: 0.5000\n", - "Epoch 27/50\n", - "2/2 - 0s - loss: 0.8093 - accuracy: 0.5000\n", - "Epoch 28/50\n", - "2/2 - 0s - loss: 0.8092 - accuracy: 0.5000\n", - "Epoch 29/50\n", - "2/2 - 0s - loss: 0.8090 - accuracy: 0.5000\n", - "Epoch 30/50\n", - "2/2 - 0s - loss: 0.8088 - accuracy: 0.5000\n", - "Epoch 31/50\n", - "2/2 - 0s - loss: 0.8086 - accuracy: 0.5000\n", - "Epoch 32/50\n", - "2/2 - 0s - loss: 0.8083 - accuracy: 0.5000\n", - "Epoch 33/50\n", - "2/2 - 0s - loss: 0.8081 - accuracy: 0.5000\n", - "Epoch 34/50\n", - "2/2 - 0s - loss: 0.8079 - accuracy: 0.5000\n", - "Epoch 35/50\n", - "2/2 - 0s - loss: 0.8075 - accuracy: 0.5000\n", - "Epoch 36/50\n", - "2/2 - 0s - loss: 0.8073 - accuracy: 0.5000\n", - "Epoch 37/50\n", - "2/2 - 0s - loss: 0.8072 - accuracy: 0.5000\n", - "Epoch 38/50\n", - "2/2 - 0s - loss: 0.8068 - accuracy: 0.5000\n", - "Epoch 39/50\n", - "2/2 - 0s - loss: 0.8064 - accuracy: 0.5000\n", - "Epoch 40/50\n", - "2/2 - 0s - loss: 0.8061 - accuracy: 0.5000\n", - "Epoch 41/50\n", - "2/2 - 0s - loss: 0.8058 - accuracy: 0.5000\n", - "Epoch 42/50\n", - "2/2 - 0s - loss: 0.8054 - accuracy: 0.5000\n", - "Epoch 43/50\n", - "2/2 - 0s - loss: 0.8050 - accuracy: 0.5000\n", - "Epoch 44/50\n", - "2/2 - 0s - loss: 0.8046 - accuracy: 0.5000\n", - "Epoch 45/50\n", - "2/2 - 0s - loss: 0.8041 - accuracy: 0.5000\n", - "Epoch 46/50\n", - "2/2 - 0s - loss: 0.8037 - accuracy: 0.5000\n", - "Epoch 47/50\n", - "2/2 - 0s - loss: 0.8034 - accuracy: 0.5000\n", - "Epoch 48/50\n", - "2/2 - 0s - loss: 0.8029 - accuracy: 0.5000\n", - "Epoch 49/50\n", - "2/2 - 0s - loss: 0.8025 - accuracy: 0.5000\n", - "Epoch 50/50\n", - "2/2 - 0s - loss: 0.8019 - accuracy: 0.5000\n" - ] - } - ], + "outputs": [], "source": [ - "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n", "loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n", "\n", "conjoined_net.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n", @@ -584,9 +473,9 @@ "history = conjoined_net.fit(\n", " x=[x1, x2],\n", " y=y,\n", - " epochs=50,\n", + " epochs=500,\n", " batch_size=(2*n_paulis),\n", - " verbose=2)\n" + " verbose=0)\n" ] }, { @@ -598,12 +487,12 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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fCg4R+aVateCSS2DuXDjkkHAnsueeMHly0pVJClBwiMiG7b47TJoETz0FW20FRxwRvt5/P+nKJEGxBoeZFZrZAjMrMbMhFfy8hZlNNbPZZvaOmfUs87Oh0XkLzKx7mf0XmNlcM3vXzB4ws3pxXoOIAIWF8M47YeLEadPCeh9XXaVJE7NUbMFhZjnAHUAPIB/ob2b55Q67DChy945AP2BkdG5+tN0eKARGmlmOmTUDzgUK3H0PICc6TkTiVqdOmKr9gw/C6oOXXx6mL1m8OOnKpIbFecfRCShx90XuvhYYD/Qud4wDDaPXjYAV0evewHh3/97dFwMl0fsB1AK2NrNawDZlzhGRmrDjjvDAA2HixDlzwujzceM0cDCLxBkczYClZbaXRfvKGgYMMLNlwGRg0MbOdfflwA3AEmAl8KW7P1PRh5vZmWZWbGbFpaWlW3otIlLeiSf+NG3JySdD//7wn/8kXZXUgKQbx/sDY909D+gJjDOzDdZkZr8i3I20AnYG6pvZgIqOdffR7l7g7gW5ubkxlC4itGwJL74Yuu4+9FAIkddeS7oqiVmcwbEcaF5mOy/aV9bpQBGAu78O1AMab+Tcw4DF7l7q7v8HPAzsH0v1IlI5OTmh6+5rr4V2kO7dYfr0pKuSGMUZHG8Crc2slZnVITRiTyp3zBLgUAAza0cIjtLouH5mVtfMWgGtgRnR8fuZ2TZmZtG582O8BhGprH32gZdfhiZNoEeP0AtLMlJsweHu64BzgCmEf9yL3H2umQ03s17RYYOBM8zsbeAB4FQP5hLuROYBTwMD3f0Hd58OTARmAXOi+kfHdQ0iUkU77wzPPQfbbAPduoUeWJJxzLOgJ0RBQYEXFxcnXYZI9njvPfjd70KATJsGLVokXZFsBjOb6e4F5fdX6o7DzM4zs4YW3G1ms8ysW/WXKSIZoW1beOYZ+PJLOOwwLRKVYSr7qOoP7v4V0A34FXASMCK2qkQk/XXsGOa2Wr48rG/++edJVyTVpLLBYdH3nsC4qA3CNnK8iAjsv3+YYXfBAjjooLDioKS9ygbHTDN7hhAcU8xsW2B9fGWJSMY4/PCwONTq1dC1K/TqFYJE0lZlg+N0YAiwj7uvAWoDp8VWlYhklm7dQoP5iBFhwOAee8CgQfDpp0lXJpuhssHRGVjg7l9EI7UvA76MrywRyTj16oV1PUpK4IwzYOTIMG379dfDmjVJVydVUNngGAWsMbMOhLEXC4F7Y6tKRDJXkyYhNObMgQMOgIsvhlatQoCsXp10dVIJlQ2OdR4GfPQGbnf3O4Bt4ytLRDJefj48+WQY59Ghw08BMmIEfP110tXJRlQ2OL42s6GEbrhPRhMR1o6vLBHJGgceGMZ8vPZamLZk6NAweeI118DatUlXJxWobHD0Bb4njOf4mDDp4PWxVSUi2adz5zDuY8aM8AjrssvguOMUHimoUsERhcV9QCMzOxL4zt3VxiEi1W+ffcI65yNHhm68ffooPFJMZaccOZ4wO20f4HhgupkdF2dhIpLlzjorhMekSXD88QqPFFKrksddShjDsQrAzHKB5wgz1YqIxOOss8KStAMHQt++MGFCWPNDElXZNo6tfgyNyGdVOFdEZPOdfTbcfnuYuqRvX915pIDK3nE8bWZTCGtmQGgsnxxPSSIi5QwcGL6fcw706xfuPGqrY2dSKhUc7n6RmR0LHBDtGu3uj8RXlohIOQMHhsdWgwbBaafBuHFgmms1CZW948DdHwIeirEWEZGNO+cc+OoruPRS2HVXGD486Yqy0kaDw8y+BipaItAAd/eGsVQlIrIhQ4fCokVw1VVhpPlpmm+1pm00ONxd04qISGoxg1GjYMkSOPNMaN48rDIoNUY9o0Qk/dSuDQ8+CO3awbHHwrvvJl1RVlFwiEh6atQoTJJYvz707AkrViRdUdZQcIhI+mrePITH55/DkUdqWvYaouAQkfTWsSMUFcHbb0Pv3pqSvQYoOEQk/fXsCWPHwksvwSGHwKpVmzxFNp+CQ0Qyw0knwWOPwbx5YVr2xYuTrihjKThEJHMccQQ8/zx89hnsvz+89VbSFWUkBYeIZJbOneGVV6BWLejSBV58MemKMk6swWFmhWa2wMxKzGxIBT9vYWZTzWy2mb1jZj3L/GxodN4CM+teZv92ZjbRzN4zs/lm1jnOaxCRNJSfH5aizcuD7t3hIc2WVJ1iCw4zywHuAHoA+UB/M8svd9hlQJG7dwT6ASOjc/Oj7fZAITAyej+AW4Cn3b0t0AGYH9c1iEgaa94cpk2DgoKwiuA//pF0RRkjzjuOTkCJuy9y97XAeKB3uWMc+HG+q0bAjyN4egPj3f17d18MlACdzKwRcBBwN4C7r3X3L2K8BhFJZ9tvD88+G9o+zjoLhg0LM+zKFokzOJoBS8tsL4v2lTUMGGBmywjrewzaxLmtgFLgX9HjrbvMrH5FH25mZ5pZsZkVl5aWbvHFiEia2mYbeOSRMBnilVeGhaF++CHpqtJa0o3j/YGx7p4H9ATGmdnGaqoF/AYYFT3e+gb4RdsJgLuPdvcCdy/Izc2t7rpFJJ3UqgV33w1DhoRHVscfD999l3RVaSvO4FgONC+znRftK+t0oAjA3V8H6gGNN3LuMmCZu0+P9k8kBImIyMaZwbXXwk03wcMPQ2EhfPll0lWlpTiD402gtZm1MrM6hMbuSeWOWQIcCmBm7QjBURod18/M6ppZK6A1MMPdPwaWmtmvo/MPBebFeA0ikmnOPx/uvz/0uurSJcxzJVVS6RUAq8rd15nZOcAUIAcY4+5zzWw4UOzuk4DBwJ1mdgGhofxUd3dgrpkVEUJhHTDQ3X98KDkIuC8Ko0WAVnERkarp3x922AGOOgqOPhqeeQbq1Uu6qrRhngU9DAoKCry4uDjpMkQk1UyYAP36Qd++4S5kq6SbfVOLmc1094Ly+2O74xARSXl9+4aVBC++GFq0gOuuS7qitKDgEJHs9uc/w0cfwfXXwy67wMCBSVeU8hQcIpLdzOCWW8Kdx7nnhhHnvXolXVVK0wM9EZGcHHjgAfjtb0Obx4wZSVeU0hQcIiIQ1i5/4glo2jQsQ7toUdIVpSwFh4jIj5o0gaefhnXrQjfdb75JuqKUpOAQESmrTZvw2GruXPjDHzQpYgUUHCIi5XXvDn/9KxQVhd5W8jMKDhGRilx8cZgMcejQMLJc/kvBISJSETMYMwbatw89rRYuTLqilKHgEBHZkPr14dFHw+tjjlFjeUTBISKyMbvuCuPHq7G8DAWHiMimdOsW1vIoKoLLL8/68NCUIyIilXHRRbBgAVx9dVh69pprQjtIFlJwiIhUhhnceWdYhvbaa2Ht2tBVNwvDQ8EhIlJZW20V1iyvUwduvDGExy23ZF14KDhERKrCDG69FerWDeHx/fcwalRWLQKl4BARqSqz8Jiqbt0wwnztWrjrrjDLbhZQcIiIbA6z0FBety5ccUV4fPXPfyZdVY1QcIiIbC6z0D139epwB3LMMVBYmHRVscueh3IiInG56ipo1w7+53/g66+TriZ2Cg4RkS1Vt25o41i6FC69NOlqYqfgEBGpDvvvDwMHwu23w+uvJ11NrBQcIiLV5a9/hbw8OP300E03Qyk4RESqy7bbhp5V8+eHEMlQCg4RkerUoweceGKYluTdd5OuJhYKDhGR6nbzzdCoEfzxj2FCxAwTa3CYWaGZLTCzEjMbUsHPW5jZVDObbWbvmFnPMj8bGp23wMy6lzsvJzrniTjrFxHZLI0bh2lJpk+H225LuppqF1twmFkOcAfQA8gH+ptZfrnDLgOK3L0j0A8YGZ2bH223BwqBkdH7/eg8YH5ctYuIbLF+/eCII+CSS2DmzKSrqVZx3nF0AkrcfZG7rwXGA73LHeNAw+h1I2BF9Lo3MN7dv3f3xUBJ9H6YWR5wBHBXjLWLiGyZH9csz82FXr1gxYpNn5Mm4gyOZsDSMtvLon1lDQMGmNkyYDIwqBLn3gxcDKyv3nJFRKpZkybw+OPw5ZfQuzesWZN0RdUi6cbx/sBYd88DegLjzGyDNZnZkcAqd9/kfZ+ZnWlmxWZWXFpaWn0Vi4hUxV57wf33h8dVp52WEcvOxhkcy4HmZbbzon1lnQ4UAbj760A9oPFGzj0A6GVmHxIefXU1s39X9OHuPtrdC9y9IDc3d8uvRkRkc/XqBSNGhDXLhw9PupotFmdwvAm0NrNWZlaH0Ng9qdwxS4BDAcysHSE4SqPj+plZXTNrBbQGZrj7UHfPc/eW0fu94O4DYrwGEZHqcdFFcMopMGxYCJA0Ftu06u6+zszOAaYAOcAYd59rZsOBYnefBAwG7jSzCwgN5ae6uwNzzawImAesAwa6e+Z1hhaR7GEWRpWXlIQAadUK9tkn6ao2i3kGPG/blIKCAi8uLk66DBERWLUKOnUKqwY+9xzklx+lkDrMbKa7F5Tfn3TjuIhIdmnSBJ54Atavh333hcceS7qiKlNwiIjUtD32gOLisPjT0UeHBvP16TPCQMEhIpKEvDx4+WU4+eSwZvlxx6XN6oEKDhGRpNSrB2PHwk03waRJ0LkzLFyYdFWbpOAQEUmSGZx/PkyZAitXhp5Ws2YlXdVGKThERFLBoYfCm2/C1lvDGWek9HTsCg4RkVSx667hsdWsWWHMR4pScIiIpJI+fcLdx6WXhjEfKUjBISKSSszg9tvhm29gyC/Wv0sJCg4RkVTTti1ceCH861/w6qtJV/MLCg4RkVR02WVhrMfAgbBuXdLV/IyCQ0QkFTVoADffDG+/DaNGJV3Nzyg4RERS1e9/D926hbuPTz5Jupr/UnCIiKQqM7jtNvj227CeR4pQcIiIpLI2bUJojBsX5rZKAQoOEZFUd8kl0LIlnHACLC+/AnfNU3CIiKS6+vXDuh1ffQVHHQWrVydajoJDRCQd7LUXTJgQelmdcEKic1kpOERE0kWPHqGx/PHHYfDgxMqoldgni4hI1Z19NnzwQRjj0bp1GCBYwxQcIiLp5oYbYNEiOPdcaNUKevas0Y/XoyoRkXSTkwP33QcdOkDfvqHdowYpOERE0lGDBqGto1GjMML8229r7KMVHCIi6apZM7j33vDY6rrrauxjFRwiIumsa9fwuOraa0OA1AAFh4hIurvxRqhVC847r0Y+TsEhIpLumjWDYcPgiSdCu0fMFBwiIpngvPMgPz98j7mhPNbgMLNCM1tgZiVm9ovFc82shZlNNbPZZvaOmfUs87Oh0XkLzKx7tK95dPw8M5trZjVzXyYikupq1w5rlS9eDH/7W6wfFVtwmFkOcAfQA8gH+ptZfrnDLgOK3L0j0A8YGZ2bH223BwqBkdH7rQMGu3s+sB8wsIL3FBHJToccAv37w4gRsHBhbB8T5x1HJ6DE3Re5+1pgPNC73DEONIxeNwJWRK97A+Pd/Xt3XwyUAJ3cfaW7zwJw96+B+UCzGK9BRCS93HBDuPs491xwj+Uj4gyOZsDSMtvL+OU/8sOAAWa2DJgMDKrsuWbWEugITK/ow83sTDMrNrPi0tLSzbwEEZE0s/POcOWVMHlybA3lSTeO9wfGunse0BMYZ2abrMnMGgAPAee7+1cVHePuo929wN0LcnNzq7VoEZGUNmgQtG8f7jrWrKn2t48zOJYDzcts50X7yjodKAJw99eBekDjjZ1rZrUJoXGfuz8cS+UiIumsdm244w7Yd99YeljFGRxvAq3NrJWZ1SE0dk8qd8wS4FAAM2tHCI7S6Lh+ZlbXzFoBrYEZZmbA3cB8d/97jLWLiKS3Ll3Cwk877FDtbx3btOruvs7MzgGmADnAGHefa2bDgWJ3nwQMBu40swsIDeWnursDc82sCJhH6Ek10N1/MLMDgZOAOWb2VvRRl7j75LiuQ0REfs48plb3VFJQUODFxcVJlyEiklbMbKa7F5Tfn3TjuIiIpBkFh4iIVImCQ0REqkTBISIiVaLgEBGRKlFwiIhIlWRFd1wzKwU+2szTGwOfVmM56ULXnV103dmlste9i7v/Ys6mrAiOLWFmxRX1Y850uu7souvOLlt63XpUJSIiVaLgEBGRKlFwbNropAtIiK47u+i6s8sWXbfaOEREpEp0xyEiIlWi4BARkSpRcGyAmRWa2QIzKzGzIUnXEyczG2Nmq8zs3TL7tjezZ83sg+j7r5KsMQ5m1tzMpprZPDOba2bnRfsz+trNrJ6ZzTCzt6PrvjLa38rMpke/8xOiBdgyjpnlmNlsM3si2s746zazD81sjpm9ZWbF0b7N/j1XcFTAzHKAO4AeQD7Q38zyk60qVmOBwnL7hgDPu3tr4PloO9OsAwa7ez6wHzAw+u+c6df+PdDV3TsAewOFZrYf8DfgJnffHfgPYWnnTHQeML/MdrZc9yHuvneZ8Rub/Xuu4KhYJ6DE3Re5+1pgPNA74Zpi4+4vA5+X290buCd6fQ9wdE3WVBPcfaW7z4pef034x6QZGX7tHqyONmtHXw50BSZG+zPuugHMLA84Argr2jay4Lo3YLN/zxUcFWsGLC2zvSzal012dPeV0euPgR2TLCZuZtYS6AhMJwuuPXpc8xawCngWWAh84e7rokMy9Xf+ZuBiYH20vQPZcd0OPGNmM83szGjfZv+ex7bmuGQOd3czy9h+22bWAHgION/dvwp/hAaZeu3u/gOwt5ltBzwCtE22oviZ2ZHAKnefaWYHJ1xOTTvQ3ZebWRPgWTN7r+wPq/p7rjuOii0HmpfZzov2ZZNPzGwngOj7qoTriYWZ1SaExn3u/nC0OyuuHcDdvwCmAp2B7czsxz8mM/F3/gCgl5l9SHj83BW4hcy/btx9efR9FeEPhU5swe+5gqNibwKto94WdYB+wKSEa6ppk4BTotenAI8lWEssoufbdwPz3f3vZX6U0dduZrnRnQZmtjVwOKF9ZypwXHRYxl23uw919zx3b0n4f/oFdz+RDL9uM6tvZtv++BroBrzLFvyea+T4BphZT8Lz0BxgjLtfk2xF8TGzB4CDCVMtfwJcATwKFAEtCFPSH+/u5RvQ05qZHQhMA+bw0zPvSwjtHBl77Wa2F6ExNIfwx2ORuw83s10Jf4lvD8wGBrj798lVGp/oUdWf3f3ITL/u6PoeiTZrAfe7+zVmtgOb+Xuu4BARkSrRoyoREakSBYeIiFSJgkNERKpEwSEiIlWi4BARkSpRcIiISJUoOEREpEr+Hx7WSur07uxoAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] @@ -624,39 +513,6 @@ "needs_background": "light" }, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tf.Tensor(\n", - "[[0.28876415 0.71123594]\n", - " [0.28876415 0.71123594]\n", - " [0.276356 0.723644 ]\n", - " [0.28192133 0.7180787 ]\n", - " [0.27187255 0.7281275 ]\n", - " [0.27187255 0.7281275 ]\n", - " [0.28490922 0.71509075]\n", - " [0.28192133 0.7180787 ]], shape=(8, 2), dtype=float32)\n", - "Model: \"model_11\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "input_25 (InputLayer) [(None, 11, 6)] 0 \n", - "__________________________________________________________________________________________________\n", - "input_26 (InputLayer) [(None, 11, 6)] 0 \n", - "__________________________________________________________________________________________________\n", - "sequential_12 (Sequential) (None, 8) 356 input_25[0][0] \n", - " input_26[0][0] \n", - "__________________________________________________________________________________________________\n", - "outer_layer_11 (OuterLayer) (None, 2) 0 sequential_12[0][0] \n", - " sequential_12[1][0] \n", - "==================================================================================================\n", - "Total params: 356\n", - "Trainable params: 356\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] } ], "source": [ @@ -666,10 +522,7 @@ "\n", "plt.plot(history.history['loss'], 'r')\n", "plt.ylabel('loss')\n", - "plt.show()\n", - "\n", - "print(conjoined_net((x1, x2)))\n", - "conjoined_net.summary()" + "plt.show()" ] } ], From c91f5c94e871032f76fe7ae8af4abe418f68bd54 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Mon, 3 Jan 2022 04:14:04 +0000 Subject: [PATCH 15/27] More realistic example --- .../quantum_advantage_in_learning_from_experiments.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 12f15dd81..3f736c906 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -295,10 +295,10 @@ "outputs": [], "source": [ "rand_source = np.random.RandomState(20160913)\n", - "n_paulis = 2\n", + "n_paulis = 7\n", "n = 3\n", "n_shots = 11\n", - "n_repeats = 2\n", + "n_repeats = 13\n", "\n", "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", "simulator = cirq.Simulator()\n", @@ -492,7 +492,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] From e2630bff5cb2ef32327e274ff58d91c19242be2d Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Mon, 3 Jan 2022 04:23:33 +0000 Subject: [PATCH 16/27] Adding some headers --- ...vantage_in_learning_from_experiments.ipynb | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 3f736c906..c48e116da 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -169,7 +169,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. The Basics" + "## 1. Creating the circuit" ] }, { @@ -242,7 +242,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Then, we create the samples." + "We then generate the sweeping parameters." ] }, { @@ -285,7 +285,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We run the code" + "## 2. Create the training data" ] }, { @@ -344,7 +344,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We encode the results so that they can be ingested by our neural network." + "## 3. Create the neural network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we create the model that encodes the measurements." ] }, { @@ -391,7 +398,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We define the conjoined model" + "We define the conjoined model that compares outputs" ] }, { @@ -426,7 +433,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We train the model" + "## 4. Train the model" ] }, { From d4a33d09a31dc3e54aa1e65d7f8bb92f735cd62c Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Mon, 3 Jan 2022 04:43:03 +0000 Subject: [PATCH 17/27] remove unnecessary includes --- ...vantage_in_learning_from_experiments.ipynb | 38 ------------------- 1 file changed, 38 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index c48e116da..d82992fcf 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -100,44 +100,6 @@ "!pip install tensorflow==2.4.1" ] }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "FxkQA6oblNqI" - }, - "source": [ - "Install TensorFlow Quantum:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "saFHsRDpkvkH" - }, - "outputs": [], - "source": [ - "!pip install tensorflow-quantum" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "4Ql5PW-ACO0J" - }, - "outputs": [], - "source": [ - "# Update package resources to account for version changes.\n", - "import importlib, pkg_resources\n", - "importlib.reload(pkg_resources)" - ] - }, { "cell_type": "markdown", "metadata": { From 93578d92b07c6875cd1f248d2d58651ef38159e5 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sat, 22 Jan 2022 20:42:06 +0000 Subject: [PATCH 18/27] Add layer names --- .../quantum_advantage_in_learning_from_experiments.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index d82992fcf..f5fa4da39 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -324,7 +324,7 @@ "source": [ "class InnerLayer(tf.keras.Model):\n", " def __init__(self, n_shots, n):\n", - " super(InnerLayer, self).__init__(name='')\n", + " super(InnerLayer, self).__init__(name='inner')\n", " self.n_shots = n_shots\n", " self.n = n\n", " self.gru1 = tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True)\n", @@ -341,7 +341,7 @@ "\n", "class IntermediateLayer(tf.keras.Model):\n", " def __init__(self):\n", - " super(IntermediateLayer, self).__init__(name='')\n", + " super(IntermediateLayer, self).__init__(name='intermediate')\n", " \n", " def build(self, input_shape):\n", " self.kernel = self.add_weight(\"kernel\", shape=[int(input_shape[2]), 8])\n", @@ -461,7 +461,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] From 26c0597ca951a75470f3c770d2ae9bd91fabb6f7 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Mon, 31 Jan 2022 04:35:17 +0000 Subject: [PATCH 19/27] Attempt to add classical shadows --- ...vantage_in_learning_from_experiments.ipynb | 48 ++++++++++++------- 1 file changed, 32 insertions(+), 16 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index f5fa4da39..63042960a 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -166,7 +166,8 @@ " qubit_pairs,\n", " pauli,\n", " n_shots,\n", - " rand_state):\n", + " rand_state,\n", + " classical_shadows):\n", " a_qubits = [pair[0] for pair in qubit_pairs]\n", " b_qubits = [pair[1] for pair in qubit_pairs]\n", " all_qubits = np.concatenate(qubit_pairs)\n", @@ -184,12 +185,17 @@ " inv_z_basis_gate(p)(q) for q, p in zip(b_qubits, pauli)\n", " ]\n", "\n", - " # Add un-bell pair.\n", - " ret_circuit += [un_bell_pair_block(pair) for pair in qubit_pairs]\n", + " if classical_shadows:\n", + " # Add measurements.\n", + " for i, qubit in enumerate(a_qubits):\n", + " ret_circuit += cirq.measure(qubit, key=f\"q{i}\") \n", + " else: # not classical_shadows\n", + " # Add un-bell pair.\n", + " ret_circuit += [un_bell_pair_block(pair) for pair in qubit_pairs]\n", "\n", - " # Add measurements.\n", - " for i, qubit in enumerate(all_qubits):\n", - " ret_circuit += cirq.measure(qubit, key=f\"q{i}\")\n", + " # Add measurements.\n", + " for i, qubit in enumerate(all_qubits):\n", + " ret_circuit += cirq.measure(qubit, key=f\"q{i}\")\n", "\n", " # Create randomized flippings. These flippings will contain values of 1,0.\n", " # which will turn the X gates on or off.\n", @@ -261,12 +267,18 @@ "n = 3\n", "n_shots = 11\n", "n_repeats = 13\n", + "classical_shadows = False\n", "\n", "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", "simulator = cirq.Simulator()\n", "\n", "all_results = []\n", "\n", + "if classical_shadows:\n", + " qubit_order = [f\"q{i}\" for i in range(n)]\n", + "else: # not classical_shadows\n", + " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", + "\n", "for pauli_num in rand_source.choice(range(4 ** n), n_paulis, replace=False):\n", " pauli = ''\n", " for _ in range(n):\n", @@ -281,7 +293,12 @@ " pauli += 'Z'\n", " pauli_num = (pauli_num - base4) // 4\n", "\n", - " circuit, sweeps = build_circuit(system_pairs, pauli, n_shots, rand_source)\n", + " circuit, sweeps = build_circuit(\n", + " system_pairs,\n", + " pauli,\n", + " n_shots,\n", + " rand_source,\n", + " classical_shadows=classical_shadows)\n", " \n", " results_for_pauli = []\n", " for _ in range(n_repeats):\n", @@ -294,7 +311,6 @@ "\n", " batch_results = []\n", " for j, single_circuit_samples in enumerate(results):\n", - " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", " out0 = single_circuit_samples.data[qubit_order].to_numpy()\n", " batch_results.append(np.squeeze(out0))\n", "\n", @@ -323,16 +339,16 @@ "outputs": [], "source": [ "class InnerLayer(tf.keras.Model):\n", - " def __init__(self, n_shots, n):\n", + " def __init__(self, n_shots, num_qubits):\n", " super(InnerLayer, self).__init__(name='inner')\n", " self.n_shots = n_shots\n", - " self.n = n\n", + " self.num_qubits = num_qubits\n", " self.gru1 = tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True)\n", " self.gru2 = tf.keras.layers.GRU(4, go_backwards=True, return_sequences=True)\n", " self.gru3 = tf.keras.layers.GRU(4, go_backwards=False, return_sequences=False)\n", "\n", " def call(self, x):\n", - " x = tf.expand_dims(tf.reshape(x, (-1, 2 * self.n)), -1)\n", + " x = tf.expand_dims(tf.reshape(x, (-1, self.num_qubits)), -1)\n", " x = self.gru1(x)\n", " x = self.gru2(x)\n", " x = self.gru3(x)\n", @@ -352,7 +368,7 @@ " return x\n", "\n", "model = tf.keras.Sequential()\n", - "model.add(InnerLayer(n_shots, n))\n", + "model.add(InnerLayer(n_shots, len(qubit_order)))\n", "model.add(IntermediateLayer())" ] }, @@ -369,8 +385,8 @@ "metadata": {}, "outputs": [], "source": [ - "input_1 = tf.keras.Input((n_shots, 2 * n,))\n", - "input_2 = tf.keras.Input((n_shots, 2 * n,))\n", + "input_1 = tf.keras.Input((n_shots, len(qubit_order),))\n", + "input_2 = tf.keras.Input((n_shots, len(qubit_order),))\n", "\n", "encoded_1 = model(input_1)\n", "encoded_2 = model(input_2)\n", @@ -461,7 +477,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -473,7 +489,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] From 9fd285d10f705bd4eedfb96fba748127cf356dc0 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sat, 5 Feb 2022 07:25:01 +0000 Subject: [PATCH 20/27] more qubits --- ...uantum_advantage_in_learning_from_experiments.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 63042960a..244f1c909 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -263,11 +263,11 @@ "outputs": [], "source": [ "rand_source = np.random.RandomState(20160913)\n", - "n_paulis = 7\n", - "n = 3\n", + "n_paulis = 3\n", + "n = 6\n", "n_shots = 11\n", "n_repeats = 13\n", - "classical_shadows = False\n", + "classical_shadows = True\n", "\n", "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", "simulator = cirq.Simulator()\n", @@ -477,7 +477,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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mdQPcf09SFuZZiKCsXu32VkQImpu9a7W3s7BI+Cjq2cRjjCsESY4UslGPQBlwGhu5sy4uYlCvuYY78ez8POefz9uoRcUBDj986EPAXnv5x3Xn6xG4UhNMn84/du6bz36WRwYBwN/+Bvzud7zf3e1e6SsMGQ0DeCNMpGMT4A7RPfYArrRWA//c53gb9Ah22YW/ExdikGR0jewffjjvT5gQ3sZJk9LL7O/Y9bsSIZA3d2nXRz/qNvCTJvFIJ7uN//7v4dcNG/ZaU+O9RLjuU1vLo7cAL51Ge7vX7wJEG2951kmT/N+/7dG4vo8ZM8KvWQjUI1AGFAml/OEP8T8jxvjpp3krC4rYVGb4y7bfuiUJG8D/6DLzd9w4/+Qh+95huIYdbtjgrrtyZXqZvMHbvPWW3+ADLH67785x6nfe8RK+bdnCo3mam3kY56uvcn0ZIfPyy57BFI/gjDOAH/2Iv7OKCh7C6HpD3bSJwx/r1/PvbcwYXorxxBP9AhHkwAP5O96wATj6aC6zQzNLlwI33eQXq9pafi75PX7oQ/y97LYbj1CS0FpPD4dxZszg52hv5+dftcotTjL6KbhIjk1jI987TAguvhg49VTgz3/msqAQRIUH774beOMNbu/RR3t/a7W1PHrN1Uclk/eSRIVAGVCiknuFETTGrrhxpnHg9n2DoSF5ozvkEOBPf4q+d6b7BjuPR4zwErm5OpY7Ovj8l77kDYGdODF9DsNHPuLt26N9hg/3PlNfn76q2IEHes8nQrDXXv7hiyNG+NspyBuwHVIaMiRaBISDD+ZQnGB7BLvuyqt6BYXAXv8X8Ax7sFNb3qarq73vQrKaBhGBi5qx3dDALxeufoTaWn7mCRO8Z5DQkISRooRg9Gjvdxc07mFzBoJDd5NAQ0PKgBJn4k6QoDF2xdQzXTfsvB0aChoi170zXddONwxkDllt28ZvznY4hSh65a8w6ur8q4I1NPjDFq7ZuvZnC40dbw8aWVd/RRK4Oq6DyPcR5hEIthDYHkHUbOvBigqBMqAUQghkxS2bTJ5G8L4y9K/QQhB88wxOlAqyaRMbkuDbYS6Gub7e37kaNPiuzJv2ORkFUyhsIQj2w/SXEIR1yrrquDrA7XaLmIkQxPEIBisqBEq/sGIFj+jp6wN+/3tvtEmuQrBwoRdftWeICp2dnLZXwittbcCvfsXx6O7u9JnHMlFI4uRAer4buXcUS5dyCKS7m+/vSrgWh6DByiVGHDSuYdd0Gcf6+sKnN4gSguDbd6FFSMgkxID3fdj9CK4wXtAjUCFQlAzstRdwwAG8qMkXvuBNnc+lj+CBB3gmq3TWuWhpAY47jtMNbN7MwvGNb3COniuuAH76U3/944/n7d57e+ETV2jAJQS2kTj1VO7YvOoqvr+kcJD49skn8/Y//iO87VVVwH77cRoIGeHjEqVMBMUjaPD324+NvWuxkwMO8GLZp52W/b1dRIWGgnHwJCdSjRgRvtAPwM8O8Agl4dvf5q0dWgr2EchooqTWDEgSFQKlX3n7bd6uXcvbXDwCWVvWxk4XAPiTobW0eGP2V6zgURvC3/7G5777XfYarr3WM+xVVelvgi4hcD2DTEiSkUG33MLeweWX8zWDy2E++CCXb93KP3vuyW+kMpoqzryIIJk8glmz+Pfgevu+6SZeGtKYeHmU4hDlEQwfzvc691w+zjTqKx9aWqJXdZsxg437H//olX3969w+W1yDoaGPf5zrHHhgEq1OFhUCpV8RwyrbXITAlUYiKvbb2up5Ht3d/oVhdtjBe/scPtzfmeoaNRI3hbQIkYSGggu7B9srbaivd78NZ+pkdpHJI+hvojwCIc4QzP5g2LDMs9NFzDo7+28GcFKoECj9yrvv8lYSmuUSGnIJgWutXKGtzW/87XH9LoMkYuCaWBZXCOT5pLM4k1HOFArJRQiCHkGcETNJEuURBMuzWcVtoLD/dqLyCxUDKgRKvyKTpcSY5+IRuFbjinojCwqBhKfCPifeSvCfWxYPCeIqk+cTQXDl15cx/3LtKCRtdDYMZo8gTAgGi0cQB/sZ1CNQSpr/+R9/TD0OS5cCd9zhPrd8OW+jhOCpp4BHHuHY66WXcr5523i7iDIcdmgI8A83jfoHDhorlxBcf703e9dGVjiT53QN/7QNc7l5BGFv0PKdqxD0LzqzWAnFGO4kq6vLLs+/rFB16qnp56STOCo0JDliLr4Y+MEPeD8qBcW8ecCcObz4SW8v8IlPAL/9rXc+6BHYxBGCSy/lDtz77vMLV0eH17kZREJgkqLCtTLWSSd5C9BkEoIvf5lFubXVPwM3Cls8Tj45uSGZcYnjEXzta5yG4cwz+6dN+WCLmQqBUrLIW1nUdPxckWtGhYbsFAdhhvxHP+KlCgFvpM78+X4haG0N/3xUbFfOXXYZbx980P+mGjd1tJ1+wOaSS+ILwY47Av/8Z7z7CbYHYC/DOVDY30GY4Rw/3vMaBzuuyWXFioaGlFDydc/DptrvuCMbZ2OihcAeuhk2jt410SpoZNrawjulswkNVVXlJgRxxsQnMW5+oPsEgsQRgmKilEJDKgRKKPmO3JBZtMHr7LMPG9TOzmghsGd2hgmBK/Zu/1PW1UWHhuJ4BEJlpf86LiFwGd+BEoKB7hMIUmpCUEqhIRUCJZR8PYLg0ozCvvvyNtiJG2TNGm/fFWMH3EJg/4NKvp2uLnee+Kix4tl4BJLtcrfd0q8Tx8gnEb8f6D6BIHbunmIPpQClFRrSPgIllHw9gpYW4Kyz0jtU99mHt/vuG51i99lnvf2wMEym0JAIQUUFTx6zs3FmIigElZXcT3DZZXw96RAGOI3zmjWckvn55/2fiyME/bUS1UBiP2Oxv0EDpRUaSlQIiGgmgOsAVAC41Rjz48D53QDcAWBkqs6FxphHk2yTEp98PYJnn2XD+cILfFxby6tvSd6d996LXiAEAP7zPzntg6SICOLyCA45hEfJTJvGncbvv89j9vMVAjm+/HJ+Bhn5NHo0r4z2r3/xvRcs8H8uSgheeIHTXCTFPfdkXhZyICh2wwmUVmgoMSEgogoANwL4FIAmAIuIaKExxlqeAt8HcJ8x5ldENAXAowAmJNUmJTvy9QjkDVDG0v/618AJJ/A8ARe9vemhmmuuYcMaZsBdHsHIkd4omQcfZG+iqyte5kmbYBpiO/9NR4fXpkce8RK0tbenXydKCGbMSHYZwhNOSO7a+VDsoRSgtEJDSfYRzACw0hizyhjTBWABgGMDdQwA+VceAWBdgu1RsiRfj2DLFv+xGMSw3PrBjmOpX1WVLgTSZ5ApT79MAuvuzr9D1v7Ht0NV9nVdfQ5JZtIsVor9DRoordBQkkLQCGCtddyUKrO5DMBXiagJ7A18y3UhIppDRIuJaPFm8ceVxMnXIwiGfbIVAqnnEgLXsokubCHI95816BHY9xBUCOJR7IYTKK0+j4EeNXQigN8YY8YD+CyAO4korU3GmJuNMdOMMdN2jMouphSUfD2CMCEIW2Sls9M/d0CMfHV1uhDINex8PS5ECLq64sfKgxlSBfvztkjaht4WCwkXqBCkU+yGM0ixP0+SQtAMwF7yYnyqzOYMAPcBgDHmeQBDAeSQeV1JgiiP4MUX+Y3oe9/jLREv+G3zy1/6j2U4Y5gQ3HQT53QXpk7lbVWV11kshn+nnXiBEddygsF7ikcQVwhkMfbgkNWwHPm2obfbI4niBtswzsFAscfUgxT78yQpBIsATCaiiURUDeAEAAsDddYAOBIAiGhfsBBo7GeQEOURSGfsj37kld19d/T15A2/ro5XGVu3DvjZzziPDgD89a/Ac8/x/gEHeInrqqq8/oYFC/g+N90E3Htv5mewPQL7n/X009OHeQq33cb3ECESwoQkzNCLEKhHkE6xv0EHKfbnSWzUkDGmh4jmAngcPDT018aY14joCgCLjTELAZwP4BYi+ja44/g0Y1yrgyoDQZRHkOlN3IUdz5dl/c4/n0f23Hefl6ANYEMtnkN1tTfxbPx4bylBmcQVhd1HYL/hT58OHHqo+zP19cCJJ6aXx/EIbFQIwsnl72cwUlNTGgvTJDqPIDUn4NFA2SXW/usAPpZkG5TcifIIXP/ImdYWCAsJiaHcuNErs/+x7DfxbBdxtz0CO01FLmPrXUJQWRkuECoEpc/YsTw8WkNDSskS5RG4RsesyzD4NyxNhBhKe0CYLQT2P1mmUUKuaxvDnc32dXIRAtdnooy8pIFWIShdZC3pYvcIVAjKgM5Ob6JTd3fmtNJ9fZwewg7V2PT0uK+xbp179TAhLBzgMpRhHkEuQgAATU35TwByvflHGXn1CEofEYKwme/FggpBGXDYYZ4BPeqozOGViy9mI3byye7zZ5zBnbVB1q/PbQ1il6F0vb1XV2f/5iXXbm8vvEcweXL0Wsk778zioSOeSxdZRCksO26xoEJQBvz977w1Jjy9g429qIuL+fP9xwccABxzjD+t9OzZ8duXySMQA55t/0DYtYHC9BFcf330ymnjxgEvvzx40zwo+XPBBfw7TjJNSH+g2UfLiEwJ3qLo6QnvFN1vPx7f393tCYGM7IlD3NBQtmGh4LXtbKG5hIaC4tHYGD1yqbIyfQiqUloQeUuzFjPqEZQRzdZ0vmzTR0SNCKqvZ6Mni80A2YVw4gpBvh6B3edRCI8gU+w/TDgVZbChQlDi2J269qieuMssClGx/7o6Nqzd3V69bN64bYMqk7Psz8t+Lh6BbfDtUUm5eATBzm4VAqVUUCEocozhNMiS8z+I7QXceae3L0Lw6qvAO+/w/pIlHP9/++3068ibvktAxCNoawMefpjLsvEI7Jm5e+2V/vl8PIKtW739fD2CICoESqmgQlDkrFgBfP7zPEvWNSd7wwZv304BIQZ96lQvxvmFLwCnnuq+j9R3JX8dMcIzrOefz9uGBs4HNG4cH8+aFf4M9pv2YYfx2/ouu3hl8vY+YkT4NcL40Ie8fbsDOxchCH6/mRLeqRCkM2wYzw5XBhcqBEWOHfppbU0/L2Vf+Yq/vKPDM2xNTbwozNq1CEXmIciC9DYNDX6jt2wZ8IlP8HWbm9mbuP/+zM8CcN6hrVv9QiBG2y6Ly9Sp/Kzbt/MoHyGX0JB8X9/9Luc+ynSNqPWQy5WWFl7JTRlcqBAUOXanrx0GEkQo9tjDX97R4Q/zbNzIYiDrCQcRQXH1FTQ0+N+w5Y1P0i9UV8c3ig0N6UncJAV1Y3A1i5gMHcqhJtvzyMcjGDUq3rhx9QjSiUrJoQwcKgRFjm2Yo4RA0h0IHR1+b0I+O326+z5S1zV6qLHR/8+dT/y9oSG9TGL7uQqBi3yEIO5C82rwlGJBhaDIyeQRyJu8SwjsUNLq1bzNRQh23tlvWPMRAlcns/RLSH9DIcgnNKRCoJQa+qda5MQJDQ0Zkj7iJugRnHUWbw8+2H2fCy7g/P2vvJJ+rqrKb/QKnWJYPIJCpmrIR6xUCJRSQ/9Uixw7NOTK/tnayiIQNHxBj0AWfjnkEH7Dt0cbAdzBd8MN6RPRPvMZ3uZr9E45JdzAXnUVMGcOMGlSfvewUY9AUTw0NFTkxPEI6urSjVLQIwB49a+hQzl5nGso6ve/n1722GO8zXdc/vz53opkQWbN4vBQIZd87I8+Ah01pBQLKgRFjgjB7ruH9xHIzF8blxBkmrAV9UZebG+/hZhHkIli+06U8kX/VIscCQ1NnAi88Ub6+bY2b+avjWuGcKYUDrKou4tCzNTtTzQ0pCge6hEUOeIRTJjAcwGCy0tm4xFkEoKwFcaA4jN6uYRtVAiUUkWFoMgRj2DCBF4dLNjJG+URBGciZwoNReXWKTaPIBdUCJRSRf9UixzxCGSy1bvv+nO5hHkE27bx8npjxnh5+oMewZ/+xItujB3L4/ttIZg3DzjiCO+4WIzeX//qdXBny+WXs7Cefnq8+sXynSiK/qkWOSIEMmEs+JYf5hG0tXEoafz4cCGYOZN/BDvh3GWX+UNFxeIRfOIT/JMLY8emr84WhQqBUixoaKjIkdDQmDG8teP+xnjDR4OGurWVRxnZKR0ypY62PYJgXTV66ejwUaVYUCEocsQjcAlBRwcnknPNI2hrYyHIJn+PLQRBI1csHkF/ouKoFAsqBEVOdzendJBsmHZoSEQhOLN41CgOB23alJ0Q2MY/2GGqRi8d/U6UYkH/VIucri428hLfF+O/fTunigDSPYKxY4EXX+T9QmX0VI8gHRUCpVjQP9Uip7ubjbCsliUewapVXp2gRzB2LK9sBgDHHcereEnO/1xRo5eOfidKsaB/qkVOd7e38MuwYZ5HsHGjV8flEQDA3Lmc0bMQWT3VI0hHhUApFrSPoMiR0BDAb/4iBHbeoaBHIJ2+hVzoRY1eOjpqSCkWYgkBET1IREcTkQrHIENCQwC/+UtoyBaCoEcgQ07FMygE6hGko0KgFAtxDfsvAZwEYAUR/ZiI9k6wTUoWdHV5CdTq6ryFwYNCYBtqWYA+U0qJbBChiZt+oRzQ70IpFmIJgTHmSWPMyQAOBrAawJNE9BwRnU5E+i44gNgewfDhPBroj39MDw3ZHsHUqbydOLFw7ZA2qPEDpkwZ6BYoSnbEDvUQ0RgApwE4E8ASANeBheGJRFqmxEI6iwHgyit5u2oVC8GhhwLPPcedyHaY4oc/BJ55Jnx94ihWrfJGHNmoR+Dx7LPASy8NdCsUJT6xuviI6PcA9gZwJ4DPG2PWp07dS0SLk2qckhm7s3jaNN7KrOEjjwQ+8pH0z1RXA4cdltv9wrwI9Qg8Ro/2cj8pSjEQd6zH9caYZ1wnjDHTCtgeJUvs0FBNDe+3tPByk3YeoaRRj0BRipe4oaEpRDRSDohoFBF9I5kmKdlgh4YA7g946y3OMVTI4aGZUCFQlOIlrhDMNsa0yIExZguA2Ym0SImksxN4803v2A4NATxC6OGHeV+FQFGUOMQVggoi71+ciCoAZFz1lYhmEtEbRLSSiC50nL+GiJamft4kopbYLS9TzjoL2Htv4L33+NgODQEsBL29vL/XXv3XLvFKTj65/+6pKEphiNtH8Bi4Y/im1PFZqbJQUmJxI4BPAWgCsIiIFhpjXpc6xphvW/W/BeCgLNpeljz5JG/b27lD0p5HAHjrBHz1q/07jLGykjOaFnJugqIo/UNcIbgAbPy/njp+AsCtGT4zA8BKY8wqACCiBQCOBfB6SP0TAVwasz1li/hlsg16BJI8LtdRQfmgI2UUpTiJJQTGmD4Av0r9xKURwFrruAnAh10ViWh3ABMBPB1yfg6AOQCw2267ZdGE0kMWUJfwT1AIJMVEf/YPKIpS3MTNNTSZiB4goteJaJX8FLAdJwB4wBjT6zppjLnZGDPNGDNtx0KkyiwBZGWyYGhIks6pECiKEpe4ncW3g72BHgCHA5gP4K4Mn2kGsKt1PD5V5uIEAPfEbEtZIyGh228HFi4EmprcHkF/ziFQFKW4iSsEtcaYpwCQMeZtY8xlAI7O8JlFACYT0UQiqgYb+4XBSkS0D4BRAJ6P32zlqquAY48Fenr8HsFPf8pbV7x+//2Bz32uf9qnKErxEFcIOlMpqFcQ0VwimgVgeNQHjDE9AOYCeBzAMgD3GWNeI6IriOgYq+oJABYYI9FvJVtsj2DePO5HcI3n/8c/gD/8of/apShKcRB31NC5AHYAcA6AH4DDQ6dm+pAx5lEAjwbKLgkcXxazDUoIuhaAoij5kFEIUvMBvmKMmQegHcDpibdKyYrqjFP7FEVRwskYGkqN5Pl4P7RFyRH1CBRFyYe4oaElRLQQwP0AtkmhMebBRFqlpNHeDlx2GdDRkX5OhUBRlHyIKwRDAbwL4AirzABQIegnfvYz4Oc/d5/T0JCiKPkQd2ax9gsMMFFZPdUjUBQlH+KuUHY72APwYYz5WsFbpDiprQ0/p0KgKEo+xA0NPWLtDwUwC8C6wjdHCWPo0PBzGhpSFCUf4oaGfmcfE9E9AP6WSIsUJ+oRKIqSFHFnFgeZDGBcIRuiRBP11q8egaIo+RC3j6AN/j6CDeA1CpR+oqcn/Jx6BIqi5EPc0FBd0g1RonEJwT77AMuXqxAoipIfcdcjmEVEI6zjkUR0XGKtUtJwCYEMKdXQkKIo+RC3j+BSY8xWOTDGtECXlexXZCEamyGp3556BIqi5ENcIXDVizv0VCkAUR6BCoGiKPkQVwgWE9HVRLRn6udqAC8l2bBSprWVjfgtt0TX+9jHgOuu432XR6ChIUVRCkFcIfgWgC4A9wJYAGA7gG8m1ahS5+23eXv99dH1Fi0Cnk+t2+byCDQ0pChKIYg7amgbgAsTbkvZ0NvL28qIb7+zk72A5tQqz1EeQdR1FEVRMhF31NATRDTSOh5FRI8n1qoSR97uKyrC68gi9CIEUX0EiqIo+RA3NDQ2NVIIAGCM2QKdWZwzYtSj3uTb2ni7bh2vQRw1aqivr7DtUxSlvIgrBH1EtJscENEEOLKRKh69vcDixcDWrennbCHYtg149930OuIRdHby+SiPwOhvQlGUPIgrBN8D8DciupOI7gLwFwAXJdes4mfBAmD6dOCUU9LP2X0E06cDY8em1xGPAGCvIOgR1NUBRx7J++PUN1MUJQ/idhY/RkTTAMwBsATAQwAciyYqwvr1vF3nSNYtRr2iAli2jPeN8cf8xSMAuJ+gpwfYaSceRVRVBQwfzmIwezYwYUIij6AoSpkQN+ncmQDOBTAewFIAhwJ4Hv6lKxULMeSuDuGuLt7afQRbtgCjR3vHtkfQ3MziUVUFTJzov9aeexamvYqilC9xQ0PnApgO4G1jzOEADgLQklSjSgEx5Nu3p58TIbBFQkYHBT8v53p6dJiooijJEFcIthtjtgMAEdUYY5YD2Du5ZhU/4hF0OAJonZ28bW/3yoJCIJ+vrfV7BIqiKIUm7jtmU2oewUMAniCiLQDeTqpRpYC80UcJwbPPemWPP85Gf/ly4LjjvM9Pngzcdhvw6U+rR6AoSjLE7Syeldq9jIieATACwGOJtaoEkDd6V2hIhMDm2mv5BwBWrwbefx+orwf23Rd45RXgsceA/fdPqLGKopQ1WS9VaYz5izFmoTGmK4kGlQpRHkGX9c194Qvpw0fXrOFwUEMDcNddXrl6BIqiJEGuaxYrGbD7CIITvmyPYPRoYMQI//nmZh522tjIxn/XXblc+wgURUkCFYKEEI+gry99MpgtBJWV6TmDmpv5p7GRj2WrHoGiKEmgpiUh7OGfHR3+NQNsIXDNM2hq4uGiQSFQj0BRlCRQjyBHWluB//5vL12E0NkJXHkl8N57wMiRXBbsJ7D7CCor00NH27ezEDQ08LF6BIqiJIkKQY5ceCH/PPSQv/zWW4Hvf5/3xYAHhSAYGrr5ZmCXXYDddwfmz+eUERMn8gpl9nVcI5AURVHyRd8xc2TbNt7aOYGCx5MmAa+9llkIjjjCn5MomKhOhGDz5vzarCiK4kI9ghypqeFtcE6A3TE8aRJvMwlBJkQINm7Mro2KoihxUCHIkTAhkKyjQLgQBPsIMiFC0NKSVRMVRVFikagQENFMInqDiFYSkXPNYyL6MhG9TkSvEdHdSbankIgQdAWm1dkhHskMGuURRC1XKUinsaIoShIk1kdARBUAbgTwKQBNABYR0UJjzOtWncngBW4+ZozZQkRFs8SKDAf9zneAV18FZs4E1q71J4+TtNLnnMPpIz7zGeDtt4F77/XqxPEIhg0rWLMVRVHSSLKzeAaAlcaYVQBARAsAHAvgdavObAA3ptZAhjFmU4LtKSi2AZ8/H1i1isNC27YBu+0GnHceMGUKcOKJwO9/Dzz4IAvBX/8afp0orr0W2G+/gjVfURTlA5IMDTUCWGsdN6XKbPYCsBcR/R8R/Z2IZrouRERziGgxES3ePEiGzgTXEF6+nOcObNwInHYacO65nE307ruBffbxPAXZSrgnrhCce663NKWiKEohGejho5UAJgM4DLz62V+JaKoxpsWuZIy5GcDNADBt2rRBsVR7sJP4nXe8/caA3DU2+oVgxAhghx34WCeJKYoy0CTpETQD2NU6Hp8qs2kCsNAY022M+ReAN8HCMOhxpZIWMglBY6M3m1iFQFGUgSZJIVgEYDIRTSSiagAnAFgYqPMQ2BsAEY0Fh4pWJdimghEcLWTjEoLNm1k87GRyQLxRQ4qiKEmSmBAYY3oAzAXwOIBlAO4zxrxGRFcQ0TGpao8DeJeIXgfwDID/Msa8m1Sb8uWRRzhT6JYt2XsEAHcmq0egKMpgI1EzZIx5FMCjgbJLrH0D4LzUz6Dnhz/k7fLlnhDccgswe7ZXp6oKGDPG/zkRgjVrgA0bVAgURRlc6MziLJBwUHU1C8HUqcBRR/nrNDQAQwLfqgjBkiWcrVSFQFGUwYQKQRbYQtDVxdu6On+dYFgI8IaKLlrkHasQKIoyWFAhyAIRgooK9ghqaoDhw70ywC0Eo0dzXREC9QgURRlMqBBkgWQW7e31hGDIEBYDySvkEgIiLn/zzfQ6OmpIUZSBRoUgC8Qj6O31QkMAsNNOwLRp/HYvghBEwkNDhgDjxqlHoCjK4EHNUBbYQiAeAQA8+iiHf+bNA/bd1/3ZUaN4O2IEewEqBIqiDBbUDGVBmBDstRdvx44N/6x0KstWhUBRlMGChoayICgEEhqKQ309b1UIFEUZbKgQZEGwj0A8gjiIAIggqBAoijJYUCHIwPbtPBEMAPr6eBsMDcVBhGDoUH+5jhpSFGWgUSHIwOzZwMEH+xeO7+1lgchGCMQTENQjUBRlsKBCkIH/+z/etrV5ZT09QHt7unGPIjgDWYVAUZTBggpBBoj8WwDYupW3QeMehXoEiqIMVlQIYmIvTdnSwttshEBSUQgqBIqiDBZUCGJiL0SzZQtvswkNBQ2+CoGiKIMFFQIAf/kLcOmlvPh8GPZCNLl4BEFECHTUkKIoA42+jwI4+2xebGbiROC009x1XEKQjUdw0EG8vfBC3qpHoCjKYEHNELwRQdIJ7GLbNm9fQkPZeARjxnjGH1AhUBRl8KChIXhG2R4iGqS93dvPxSMIu6cKgaIoA40KAbywT1whyMUjCKJ9BIqiDBZUCAC0tvq3LpLyCILrGyuKovQ3ZW+GOju9lcfa2jif0PnnA2+95a8XFILKyuxSTIRhT1RTFEUZCMpeCOxwUGsrsGwZcPXVwJe+5K8XDA3V1+dnxP/8Z+DMM/PzKhRFUQpB2XdV2uGgtjbPuNsTyAC/EHR15dc/AAAf/jD/KIqiDDTqEbT590UIenvD6wH5C4GiKMpgoeyFQDyCsWN5X/oLZO0BwfYIAA3pKIpSOpS9EMibfmMj78tQ0kxCoB6BoiilggqBJQQbNgA338zHEhoSD0E9AkVRSpWyF4Lt23n7jW/w9umneSsegXQaq0egKEqpUvZCIIb+wAOBPfbwJouJEEioSD0CRVFKlbIXAgn9VFUBtbVe4rlMQqAegaIopULZC4F4BNXVwNChXt+AbMNCQ+oRKIpSKpS9EAQ9AqGvj5enFEFQj0BRlFKlrGcWn3wycPfdvO8SgvPP947tNYsBFQJFUUqHsvYIRASAdCHo7QXWrOH9PfdM/+wOOyTbNkVRlP6irIVAqKzk1BJBj6CjA5g+HWhoSP+MXVdRFKWYSVQIiGgmEb1BRCuJ6ELH+dOIaDMRLU39nJlke8KoquKtSwhqa91GX4VAUZRSIbE+AiKqAHAjgE8BaAKwiIgWGmNeD1S91xgzN6l2ZEMwNNTRAYwezaOJouoqiqIUM0l6BDMArDTGrDLGdAFYAODYBO8XyerVvAaALDNpI6uF2cb9/fc55YR6BIqilDpJCkEjgLXWcVOqLMgXiegVInqAiHZ1XYiI5hDRYiJavHnz5pwac//9wGc+4x8JJLiEAADWrlUhUBSl9BnozuI/AJhgjNkfwBMA7nBVMsbcbIyZZoyZtuOOO+Z0o5NO4r6AjRtd1+etGPehQ/39BioEiqKUMkkKQTMA+w1/fKrsA4wx7xpjUkkccCuAQ5JqTGMj8LGPeesPiPEHvHQSYtyHDAFmzPDKpLzS6lFRIVAUpVRIUggWAZhMRBOJqBrACQAW2hWIaBfr8BgAyxJsD+rqvLTT77/vlQc9gr4+b8KYLQTiJdh1FUVRip3ERg0ZY3qIaC6AxwFUAPi1MeY1IroCwGJjzEIA5xDRMQB6ALwH4LSk2gNwfiARAnut4kxCIKOGbC+ipibJliqKovQfiaaYMMY8CuDRQNkl1v5FAC5Ksg02dXUsAK2t/lnFwdBQb69n6G0hsNNMyNrGiqIoxU5Z5RoSj+Ab3wB++9v085JK4qijvP4AWwjU+CuKUooM9KihfqWujtcXWLHCfX7aNF6xbOFCrz+gpsbzFEaN6p92Koqi9CdlJQSyhkDUm31NDZ8Xj6CvzxOC0aOTbZ+iKMpAUFZCkE3qaBGC7m4VAkVRSpuyEgLxCGTB+igkNNTd7XUSqxAoilKKlJUQiEewbl3murZHIPmJVAgURSlFymrUkBhySVd03nnA1Ve7637rW8BTTwGnn879BrfcAlxyCc9Qrq7un/YqiqL0B2TsWVJFwLRp08zixYtz+uz69d4iMzNnAr/5DbDzznxcZF+DoihKVhDRS8aYaa5zZRUaGjcOqKjg/fp6nR2sKIoClJkQVFQAu6SyG9XVqRAoiqIAZSYEAMf4AfYINNavKIpShkIgfQR1dV6YSFEUpZwpq1FDAOcZqqkBjj+ej6+9FjjssIFskaIoysBSVqOGFEVRyhUdNaQoiqKEokKgKIpS5qgQKIqilDkqBIqiKGWOCoGiKEqZo0KgKIpS5qgQKIqilDkqBIqiKGVO0U0oI6LNAN7O8eNjAbxTwOYUA/rM5YE+c3mQzzPvbozZ0XWi6IQgH4hocdjMulJFn7k80GcuD5J6Zg0NKYqilDkqBIqiKGVOuQnBzQPdgAFAn7k80GcuDxJ55rLqI1AURVHSKTePQFEURQmgQqAoilLmlI0QENFMInqDiFYS0YUD3Z5CQUS/JqJNRPSqVTaaiJ4gohWp7ahUORHR9anv4BUiOnjgWp47RLQrET1DRK8T0WtEdG6qvGSfm4iGEtGLRPSP1DNfniqfSEQvpJ7tXiKqTpXXpI5Xps5PGNAHyBEiqiCiJUT0SOq4pJ8XAIhoNRH9k4iWEtHiVFmif9tlIQREVAHgRgBHAZgC4EQimjKwrSoYvwEwM1B2IYCnjDGTATyVOgb4+SenfuYA+FU/tbHQ9AA43xgzBcChAL6Z+n2W8nN3AjjCGHMAgAMBzCSiQwH8N4BrjDGTAGwBcEaq/hkAtqTKr0nVK0bOBbDMOi715xUON8YcaM0ZSPZv2xhT8j8APgLgcev4IgAXDXS7Cvh8EwC8ah2/AWCX1P4uAN5I7d8E4ERXvWL+AfAwgE+Vy3MD2AHAywA+DJ5lWpkq/+DvHMDjAD6S2q9M1aOBbnuWzzk+ZfSOAPAIACrl57WeezWAsYGyRP+2y8IjANAIYK113JQqK1V2MsasT+1vALBTar/kvodUCOAgAC+gxJ87FSZZCmATgCcAvAWgxRjTk6piP9cHz5w6vxXAmH5tcP5cC+A7APpSx2NQ2s8rGAB/JqKXiGhOqizRv+3KXFuqFAfGGENEJTlGmIiGA/gdgP80xrQS0QfnSvG5jTG9AA4kopEAfg9gn4FtUXIQ0ecAbDLGvEREhw1wc/qbjxtjmoloHIAniGi5fTKJv+1y8QiaAexqHY9PlZUqG4loFwBIbTelykvmeyCiKrAI/NYY82CquOSfGwCMMS0AngGHRkYSkbzQ2c/1wTOnzo8A8G7/tjQvPgbgGCJaDWABODx0HUr3eT/AGNOc2m4CC/4MJPy3XS5CsAjA5NSIg2oAJwBYOMBtSpKFAE5N7Z8KjqFL+X+kRhocCmCr5W4WDcSv/rcBWGaMudo6VbLPTUQ7pjwBEFEtuE9kGVgQjk9VCz6zfBfHA3japILIxYAx5iJjzHhjzATw/+vTxpiTUaLPKxDRMCKqk30AnwbwKpL+2x7ojpF+7ID5LIA3wXHV7w10ewr4XPcAWA+gGxwfPAMcG30KwAoATwIYnapL4NFTbwH4J4BpA93+HJ/54+A46isAlqZ+PlvKzw1gfwBLUs/8KoBLUuV7AHgRwEoA9wOoSZUPTR2vTJ3fY6CfIY9nPwzAI+XwvKnn+0fq5zWxVUn/bWuKCUVRlDKnXEJDiqIoSggqBIqiKGWOCoGiKEqZo0KgKIpS5qgQKIqilDkqBIqiKGWOCoGiKEqZ8/8vYeX/c3RpKAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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" ] From 51764caec8ec46da0413df31ace2e37749cdd39f Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sat, 5 Feb 2022 20:55:26 +0000 Subject: [PATCH 21/27] Classical shadow does not NaN --- ...dvantage_in_learning_from_experiments.ipynb | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 244f1c909..b533d3a3e 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -264,7 +264,7 @@ "source": [ "rand_source = np.random.RandomState(20160913)\n", "n_paulis = 3\n", - "n = 6\n", + "n = 3\n", "n_shots = 11\n", "n_repeats = 13\n", "classical_shadows = True\n", @@ -279,19 +279,17 @@ "else: # not classical_shadows\n", " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", "\n", - "for pauli_num in rand_source.choice(range(4 ** n), n_paulis, replace=False):\n", + "for pauli_num in rand_source.choice(range(3 ** n), n_paulis, replace=False):\n", " pauli = ''\n", " for _ in range(n):\n", - " base4 = pauli_num % 4\n", - " if base4 == 0:\n", - " pauli += 'I'\n", - " elif base4 == 1:\n", + " base3 = pauli_num % 3\n", + " if base3 == 0:\n", " pauli += 'X'\n", - " elif base4 == 2:\n", + " elif base3 == 1:\n", " pauli += 'Y'\n", " else:\n", " pauli += 'Z'\n", - " pauli_num = (pauli_num - base4) // 4\n", + " pauli_num = (pauli_num - base3) // 3\n", "\n", " circuit, sweeps = build_circuit(\n", " system_pairs,\n", @@ -477,7 +475,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] From 2c690949a7df56927a2d17bb61e2862be074ba22 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sun, 6 Feb 2022 21:18:11 +0000 Subject: [PATCH 22/27] Restore I,Z,X,Y --- ...vantage_in_learning_from_experiments.ipynb | 36 ++++++++++--------- 1 file changed, 20 insertions(+), 16 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index b533d3a3e..a8f360ee6 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -215,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -267,7 +267,7 @@ "n = 3\n", "n_shots = 11\n", "n_repeats = 13\n", - "classical_shadows = True\n", + "classical_shadows = False\n", "\n", "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", "simulator = cirq.Simulator()\n", @@ -279,17 +279,21 @@ "else: # not classical_shadows\n", " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", "\n", - "for pauli_num in rand_source.choice(range(3 ** n), n_paulis, replace=False):\n", + "paulis = []\n", + "for pauli_num in rand_source.choice(range(4 ** n), n_paulis, replace=False):\n", " pauli = ''\n", " for _ in range(n):\n", - " base3 = pauli_num % 3\n", - " if base3 == 0:\n", + " base4 = pauli_num % 4\n", + " if base4 == 0:\n", + " pauli += 'I'\n", + " elif base4 == 1:\n", " pauli += 'X'\n", - " elif base3 == 1:\n", + " elif base4 == 3:\n", " pauli += 'Y'\n", " else:\n", " pauli += 'Z'\n", - " pauli_num = (pauli_num - base3) // 3\n", + " pauli_num = (pauli_num - base4) // 4\n", + " paulis.append(pauli)\n", "\n", " circuit, sweeps = build_circuit(\n", " system_pairs,\n", @@ -332,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -379,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -470,12 +474,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] From 70a67a8b20bbb0f494f375eaaf189884dc4923d7 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sat, 12 Feb 2022 21:42:12 +0000 Subject: [PATCH 23/27] upgrade version --- .../quantum_advantage_in_learning_from_experiments.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index a8f360ee6..58b1b522c 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -97,7 +97,7 @@ }, "outputs": [], "source": [ - "!pip install tensorflow==2.4.1" + "!pip install tensorflow==2.7.0" ] }, { From bbe8e376b492710721b1a2a595d53a0ddf34c169 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Sat, 12 Feb 2022 22:06:02 +0000 Subject: [PATCH 24/27] Untangle param and circuit building. More to do. --- ...vantage_in_learning_from_experiments.ipynb | 48 +++++++++---------- 1 file changed, 23 insertions(+), 25 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 58b1b522c..ae20ef9ec 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -165,15 +165,12 @@ "def build_circuit(\n", " qubit_pairs,\n", " pauli,\n", - " n_shots,\n", - " rand_state,\n", + " flip_params,\n", " classical_shadows):\n", " a_qubits = [pair[0] for pair in qubit_pairs]\n", " b_qubits = [pair[1] for pair in qubit_pairs]\n", " all_qubits = np.concatenate(qubit_pairs)\n", "\n", - " flip_params = sympy.symbols(f\"param_0:{len(qubit_pairs) * 2}\")\n", - "\n", " # Add X flips.\n", " ret_circuit = cirq.Circuit(cirq.X(q) ** p for q, p in zip(all_qubits, flip_params))\n", "\n", @@ -184,7 +181,7 @@ " ret_circuit += [\n", " inv_z_basis_gate(p)(q) for q, p in zip(b_qubits, pauli)\n", " ]\n", - "\n", + " \n", " if classical_shadows:\n", " # Add measurements.\n", " for i, qubit in enumerate(a_qubits):\n", @@ -197,13 +194,7 @@ " for i, qubit in enumerate(all_qubits):\n", " ret_circuit += cirq.measure(qubit, key=f\"q{i}\")\n", "\n", - " # Create randomized flippings. These flippings will contain values of 1,0.\n", - " # which will turn the X gates on or off.\n", - " params = create_randomized_sweeps(\n", - " pauli, flip_params, n_shots, rand_state\n", - " )\n", - " return ret_circuit, params\n", - "\n" + " return ret_circuit\n" ] }, { @@ -215,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -258,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -295,13 +286,20 @@ " pauli_num = (pauli_num - base4) // 4\n", " paulis.append(pauli)\n", "\n", - " circuit, sweeps = build_circuit(\n", + " flip_params = sympy.symbols(f\"param_0:{len(system_pairs) * 2}\")\n", + " \n", + " circuit = build_circuit(\n", " system_pairs,\n", " pauli,\n", - " n_shots,\n", - " rand_source,\n", + " flip_params,\n", " classical_shadows=classical_shadows)\n", " \n", + " # Create randomized flippings. These flippings will contain values of 1,0.\n", + " # which will turn the X gates on or off.\n", + " sweeps = create_randomized_sweeps(\n", + " pauli, flip_params, n_shots, rand_source\n", + " )\n", + " \n", " results_for_pauli = []\n", " for _ in range(n_repeats):\n", " results_for_repeat = []\n", @@ -336,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -383,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -418,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -474,12 +472,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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R1RADRDr16ROevny5Dc1xyy11mx8iolpggEi3M85ITFuyxObTptVpVoiIaoMBIt2eew7o2TM2zQ3HUVhY9/khIqohBoh0KygAunaNTXNDbTRpUvf5ISKqIQaIKDRrFp7+zjvA00/XbV6IiGqIASIKDz4I9OgRvm3EiDrMCBFRzTFAROHAA22wvs8/B666KtO5ISKqkUgDhIgMFpGlIrJcREYl2ec8EVkiIotFZEIgfbiIfOpNw6PMZ2S6dwdatoxNC1Y/zZ4N/OlPwOuvA7/9LR+oI6J6JbLB+kSkIYCxAE4AsBrAPBGZpqpLAvv0AnA9gKNUdb2IdPDS2wK4FUARAAUw3zt2fVT5jUyLFrHr3br5y9//vs27dwe++MKqpa68ss6yRkRUlShLEAMBLFfVFaq6A8AkAKfH7XMZgLHuxq+q7if0iQBmquo6b9tMAIMjzGt0XIC4915g6NDwp6m/+MLmn3xSd/kiItqNKANEFwCrAuurvbSg/QHsLyLviMgcERlcjWMhIiNFpFhEiteEjaJaH7gA0bWrLW/ZYuv33JO478cf112+iIh2I9ON1I0A9AJwDIChAJ4QkdapHqyq41S1SFWL2rdvH00Oa6tNG5t362avJ92yBXj3XWBUXJNMv36JAWL7dgsqEyfWSVaJiIKiDBAlAAIV7ujqpQWtBjBNVXeq6koAy2ABI5Vjs8OgQfbsw5FH2mtJy8qAv/89dp/jjgPOPhv4+mtg61Y/vaTEAsrvfle3eSYiQrQBYh6AXiLSU0QKAAwBED8Y0RRY6QEi0g5W5bQCwAwAg0SkjYi0ATDIS8s+BQXAhRcCDRpYCUIVmDrV337rrcCrr1pDNeAPy7FxI3DOObac7ME7IqIIRdaLSVUrRORK2I29IYDxqrpYREYDKFbVafADwRIAlQCuU9W1ACAid8CCDACMVtV1UeW1zuyxh81LSoDHHrNgMWKEvT9i771t25dfAvvvb9s/+MDSGCCIKAMifSe1qk4HMD0u7ZbAsgL4jTfFHzsewPgo81fnXIAAgEsvBRoF/vyuBPHllzZv2tTf1ry5zV94ATj88Niusql45BHg6KOBQw+tfp6JKG9FGiAojrvRFxXFBgcA6NLFShLLllk7RUWFv62gACgvB84919Z/9CNg1qzUP/dXv7K5GzSQiCgFme7FlF/+9z+b9+2buK1xY2Cvvaz7a8uWwNq1/rayMquWct58M/XPDAYaIqJqYICoS6edBhx0EHDTTeHbXTvErl3AihV++saNfuN1dW3fXrPjiCjvMUDUpc6dgYULgf32C98ebFt4/nmgd29rq9i4EVi9OnbfXbv8EklVtm3zl1nFRETVwABRnwQbplWtqqllS2DTpsQSRMOGQPv2wPz5VZ8zGCA++yx9eSWinMcAUZ+4cZrOPNPm770HtGplbRCffhp+zEsv2fyzz/xusUHBAHHddenLKxHlPAaI+uSCC2x+9902v/hi690EJH8TnRueY7/9gMMOS9weDBAcDJCIqoHdXOuTE0/02wm2bbOeTQAwYYK9MyLM7qqNXIDo3h345pv05JOI8gJLEPVVkyY2PEeDBsDxx1vavvva/P77/f3mzbPnIpJxAaJHD2DDhtgSBRFRFRggssEVVwA//zkwZ46VMOJ7Qb31lr8cHwDces+eNv/22+jySUQ5hQEiG7RsCfz5z0C7dv56MuvjXroXLEEANmIs5bd//hO48cZM54KyAANENmKAoNqYOhUYNy7TuaAswACRjaoKEGvX2vsm5s4FTjrJHrIDgH32sXlJdr5Wg9Joxw7/zYZEVWAvpmxUVYB48kngmWf8ddf20L07UFjov/+a8tfOnVayrKhIHDSSKIAliGwUDBBlZbHb4offcFVKTZvaWE+ffx5p1igL7Nhh8/jvTm199VV6z0cZxwCRjZo08ZebNbNhwH/7W1svLo7dd8oUmxcWWimipiWIHTuAJ57wn/am7LVzp83TWc00dao91DlzZvrOSRnHAJGNROw91a++auvPPw/ce68FitLS8GMKC62huqoAMX06cPvt4dvGjAFGjgQmTqxV1qkecCWIdAaIt9+2+YIF6TsnZRwDRLYaMwb48Y9j004+Ofn+jRpZCaK01F4+FOYnPwFuuy18mwssW7dWO6tUz0QRINw5CwrSd07KOAaIXNKxY/JtIn5X12nTqn9uFxiCI85Sdoqiismd0w0PQzmBASKXdOpU9Xb33uuhQ+2p7GTcr8EgV+oYO9aCzY4dFjSCT3Gngu+kyLwoShAMEDmJASKXJCtBzJ1rc1eCAPyB+7ZuBVq3tnYMp6zMpuA7KFwJwgWW9euBX//axoFatiy1/C1ZYmNLuSHKs9X27XbtwdfCZpMoSxANeEvJJfzXzCXt2yemffIJ8L3v2XLnzn76mjXWI+mGG+xhumDjdFkZcPrp1i3W/eKPb7fYssXejgekPkqsa8AMPqfhrFxpb8+bMSO1c2XSCy8ADz1kHQWykStBbN6c/nPyFbc5hQEil8QX79u0AQ44wF9v2NB/adDXX1uvpQcftHX3QB1gN//XXrNl9ys5LEAUFtryhg2p5a9VK5uvW2fB6aab/Oc2nnwS+MtfgPHjUztXJlVU2Dxbb4ZRVjFl69+EQjFA5JoDDgB++lNbdsODB/3hD0DbtsCbb9oIsU5wjKbgjePLL20e33vprbf8AJHqCLHuJrJ+vfWXv+su4KqrYj9/3brkx7/xhrV/ZHo8KRGbZ2t7SpRVTLk8nPymTf4LuvIEn7PPNe6tccOGAYcfHr5Pp06xLyA66SS7+TpbtthNUBUYNAhYvTqxBPGrX/nLqQYIF2TWr/dvKJs22dxVU1VVr//QQzafPRs466zUPjMKLkBkqyhKEK7kkMsB4qKLgMmT7UdMmzaZzk2dYAkiV512WvJG6+bNY9c7doz9j71liz2hDdgNe86c5A/gAeFtEKtWxTZeV1T4QWbdOmDXLlt281QChGsAdcdkiqtiytYSRBQBwp0rl6uYXGePF17IbD7qUKQlCBEZDOAhAA0BPKmqY+K2jwBwLwA3xOijqvqkt60SgNcKii9V9bQo85pXHn3Ubt5vvgkMHJj42tItW2Jvfq+8UvUDcmEBont3O8euXcAHHwADBgAHHmjbNm3yR5l1n5NNAcKNYZStASKKKibX4J3LJYjWra00/f77mc5JnYksQIhIQwBjAZwAYDWAeSIyTVWXxO36nKpeGXKKraraL6r85bWBA2264AJb//OfY7cPHWrzww6z/wwvvlj1+T76CPj0U6BXLz/N3TxnzQKOO86WXfXXrl1+w/auXdZgXVpqVTdlZXaTce0bgPW0at3ar9pJZ++bmog6QOzYASxeDPTvH935gWgCRK6WIF55BVi0yJaTjUSQg6KsYhoIYLmqrlDVHQAmATg9ws+jmho+HBg1Cnjssdj0YcNsvmwZ0K1b8uM//RTYf3+/6iVo0qTwY1yVlQsOlZVA376WFl+K+P3vrUupK0Gk2mvKKS+3UlO6Sh4uQEQ1cOFvfmPBeeXK9J9bNZoShHtRVa6WIE480V9mgEiLLgACT1phtZcW72wR+UhEXhCR4F2oUESKRWSOiJwR9gEiMtLbp3jNmjXpy3m+adrUbsIXX+ynDRhgjdeu62ywdBAUfJ/A9On2Kz/YAD5lip0j2KgN+A/hbd7sj0B77LE2Hz7c6nuvuy72BUfuBl/dAHHTTdZbqiZDjIRxASLdw2U777xj8+peZyqCQTxdAaKy0s9rrpYg3CgEHTpE9+9eD2W6kfpfAHqo6iEAZgJ4OrCtu6oWARgG4EERSeizqarjVLVIVYvahz0kRtVTUGBPRv/lL3bT7tPHfw92sgDRu7e/fLpXQBw/3q8iKi21J7j79Ik9zj0st26d9Upq1MjvmfTaa9YD6777gK5d/WPcMxOu/SJVwa66H34I3HOPDUp40UUWlFxPqlRFHSBcySSK3lLBYVTSFSDWr/er23K1BNG4sVW99u7NEkSalAAIlgi6wm+MBgCo6lpVdT85ngQwILCtxJuvADALQEQVshRj1qzYkoRrnN5vv/D9L7vMgkhwEL82bWJ/Se67b+xT3EHr1lk7x0EHAT/4QdV5W+I1XwV/WW/ZAsyfX/VxLi/l5UC/fladdvvtwFNP2fW6ksWyZcA//lH1udxnBufV9ctfAhMmJN/ufuVv22bdj9P5Ih5XvQSkL0AEqwSjLEH07OlXe9a18nLr2desGQNEmswD0EtEeopIAYAhAGLK+CISvGucBuBjL72NiDTxltsBOApAfOM21QXX3fWMM8K3H3OMDdtRXg789a+W9uijsQ24PXokH0hw/XprkN1nHytFuHdnOzfd5C+7dotggPjDH4CiIv/dGEEzZgDHH+8P8ZFsSJDFi23evz9w3nkWFKdMSV5CcOk1ucGqWqeA889P3nXYlSDKyqyBf+DA6n9OMlGUIIIPN0ZZgvj888y9j6SszLqHN2/OKqZ0UNUKAFcCmAG78T+vqotFZLSIuC6rvxKRxSLyIYBfARjhpfcGUOylvwFgTEjvJ6oLL79sz0EESxDBKplg1dOIEdZYHa9Ll/AAccklNl+92m8Ef/994IorbFkEuOMOawQPcu0Xs2cDf/ubLT/8cOL5L7vMqqtWr7b1pUttHv/ejCVL7Few+2X4zDPAmWcmtps4tQkQwZtL8OHEIBcgXEArKQnfL8y99/rDp4RxJYiWLdNfgmjVKroSRPAHRya6F7sAkYkSxMsvJ3YgqSuqmhPTgAEDlCJm/zVt+bzz/OWgPfbw93PT+PGq27Ylpn/0kb98333+OWbMiP2srVsTj503L3a9Sxfbd9Qo1fvvt2MaNozd59BDbT5xouq++9ry975ny/37+/udcYbNf/jDxOvbtUu1qMi2N26sWlZWvb/hF1/4nyOi+uabqq1bq37+uerMmaoffqjao4dtv/PO2L9DKna3/4oVtt19RmVl9fIf5qmn7Fy9e6tG9f9w40b/2kpLo/mMZHbssM+94w7VX/xCtUOHuv386n4Hqn16FGuS+2qmG6kpW02aFN7Nc8CAxLQuXWLfo+0ESyXBbrTf/37sfoWFNtAgANx8M9CihT9CrVNSYkN+jBkDXHutlQri8+dGn+3Y0doeHn7YShMrVtjDfGedZe0n//qX7RfsoQXYf9MRI6wB/+CD7df4iy/6Y0Nt3Gh5f/NNW3//fb/KpbLSemYFq2NUrVPAhg32no0TTgCOPtrPd7B6LV1cFdOee9o8HdUlrgSx116JJYhZs/xeWbUR/Lu58ZBUrZR54421P39V3N8oUyWIDGKAoJoRCR/7/8UXbfymoGBPpKCmTW3gQCA2QOyxh1Xx3HWXn+ZumkceCfziF+Hne+IJfzn4fgvAAorrJtupk+Xpqqvs2QtXZfH739t+7rN27rT2AtdovHmzVT9ddpnd9Bo3Bi680G6MqsDTT1u116mn2vsiBgwAHnjAjh0/3npmuQESJ08GzjnHz597GHHjxvDnSeKr2cIEq4ySVcO4Kib3d09HV9p16yyAd+uW+AzLscfuvvNBqp/huCeZ3Y367rtrf/6qBAOEa4PIRDVXBl73ywBBqfv4Y7sBVmXPPYF//9tu0O7GsNdeNp892/8V/7Of2XzxYuDOOxNLBJMn2xPU8fr2tcAxa5afNmCA9U66+WY/7Z57bDyqJ56wIT7uv9/fFhyjynW/7d3b2k+CJaC337YeR65h1DUq/+AHVoo56CB/32nT/N5Qmzf7Awvedx/w7rv+m/fceD777gucfbZ//IoV/nLYaLXnn2+9mc45x4LInXcCzz1n26691oKya4wHknfddSWIoiKbX3CB3zYDWMmrW7fYdo8vvgBuucUC7ObNwLPPxj50uHatBZz997e8b95s3YmDN1FXKgOARx5J3v4S5ttv7XyOe27GPZwXlccft8DtApHrxaSavK1lwgT//e219Z//AEcc4a9n4gVVyeqesm1iG0Q9VFqq+q9/JaZv3Ki6c2f1zvWTn1g97K5dftrUqX7bwvjxie0UH38ce46xY1WvvDL2HNu3WxvAbbfZ+muvJZ6na1fV9etV33nH1v/zH9t3zhyrd+/USbWwULVVq8Rj3bTffrHrq1YltqMkm3r0sM+47DJbf/zx2Hrpxo1tedAgP33pUtu2dq3q66/71/vf/9r2l17y923VytqIVFUvvdTSHn3UP+a44yytuFj16qttefJkf/u556oeeKClAapjxtj8z3+OvY5Nm2z/+Dr1O+9UHTw4+b99YaF/TM+e9lmqfhtW48bJjw2qrLQ2IFX77qxcWfX+7jM/+MC/5ocesuW1axP3X73atu21V+z3pKY6d479+y1YULvzJYEq2iAyfmNP18QAkePKy1W/+SY2bc4c+wr36WM3nw4dVO+6yxp833gj9XOXllpDpDN7tur558f+57zkEr+Re/782ONXrPBv0mefndpNf8sW1Q0bYtP69g3f97bbYtfvvddfPv748GOOOsoC4Vln2fprr1le3U17xQrVRx7xb/6TJqkefrj9LQHVhx/2r++wwyzt2WdVDznElk891d9+3HH2eYsXx94gTzopNk8vv2z/TsEA0aGDv37RRarDhlm+V62yNPcjwE0jR1rj/qZN1sAPqDZrltq/88032/4TJtj8mGOS71tW5n/m22/bfMYM1SeftOUvv0w85umnY/N65pmp5SvekCGqf/ub6hFHxJ4vGOjTiAGCctOSJfYVPvhgW6+oSN+5y8rsF/OiRYk9s1atStz/8stt28yZ1oPqj3/0f4276dRT/WVXigluHzZMtUGD2LRx4/xeQm4699zEgBD/axOwUlVBgS0XFqp++qnqz39uJSb3+du22Xr8saNGWWD45S9je3i5qVs3C6yPPGKlqFNPtdJY167+Pr17xx4zaJDqX//qrzdtmnheQPXVVy0YAX7J0U3/+IfN33xT9cEHbblVq8R/D9c7a8MG+zdZty7xc9z3xlm40C9JBXvYvfKKzf/7Xz+4fPKJ//dbuFD12mutZBM8/1lnVf97t2WLf3x88D/0UAvwjiuR1RIDBOWmigrVK66wX65Rcr8g3eRuIkFr11pVSbAkUl6u+t57/nHu13vTpv4+ixap9utn6ddck3gTU7VfjmE30uD0f//nL998swWGZs1s/e67bX7DDVZKif/l/Nhjiec75RR/uWXL2G1hJZ0RI+xc06fvPq+pTK5kcfTRftqxx1opMixw9O5tVUGq1s0ZUN282e+Ove++1u05eC2NGqn+7GdWFelKBg89ZOd48UV/vxdesPn77/tVdJdfblVK7dsnv4ajj1b96isLIJ99ZgH32GNV586N/ftv2KC6fLktFxf7xx98cPh5d+70fxw99phd7/btNfpqqyoDBFGtvf++/x+0uqZPtz707vmOAw6I3T5unKWPH2/7/OEPsZ/lqlvipylT/OcZXHvGCSfYMSNG2HpBQeyvUheogoLVPsmmsWP95WAwctM119i5vv02cduXX/o36ppOd9/t3wSDpZT46fbb/bagp59WPeggf9tjj1lVYfwx55zjB+lhw+wzbr3V3/7HP9r8k0+s5BVs6wlOjRqpNm/ur7dr51fhuWANWBVckNtn+/bYaqpkwefhh/2A5qbRo6v/vfQwQBClw69/bfXCNeVKE5dfnrittDT2obX4YLR2req776r+5jeWvt9+9kty4UK76e3cadUiGzbY/q4h+5RTbN3d1C+9NLaU47iH/4DE9hcgtopm5szwm2x83p991vKs6jdu726Kf7gRsIcg16zxzx+sqqtqOu00a1dx65WVFqjd3yXsRt+/f+L1XXihzd3fdufOxOP22stKtC4Qtm2bPF9Nmlhb2SmnWEBy6VdcYVV3NQmghxwS2/miGhggiOqLyZPDq6jitWtnDcY1tW2b/RrdutVPq+oGUlFhN9Dycv9p68svt95M48bZPr/9rVX9VFZa28PChdZbCrDSjzNhgt1kgzZssF/3Rx1lTyNXVtqNP/5Gt2iRHb98udX9Dx+eGNBckAxORx9tQbBnT034xe5KAaqqTzxh6xdcYOvBklH8zdlVc3XqpNqmTWwe3D6uHcQ9XX3xxbZ+ww2q3bvbcrLOBzWZwqoh+/Wr/hP9MZfCAEGUXSor0zMMRk2tWpX659e0/nvBAtWrrrLPGTbMGqdTsXat6vPPW7WfCwSu19XSpRacXGMyoHrjjf6xrivu1Vf7aSUl1ngf7OI8eLCVfvbe278JBw0ZYumuTcQNFePSH3/chnEB/CpEN8V3RIifBg+2+aRJqtdfb8u/+51VPYa1Rz34YM3+/h4GCCLKTT/9qd3G/v732PTKSnsGZ+LE2Oqpigq7mSfrAXTffarPPeevT5xo5+/cOXa/8nL/WZPKSr90dvLJtv+UKdYTDLBu01OnWtCYMsWexXjuOesGG3+zv+02y2N5uZ1vyxYrjbj1YBuPq/pav75GfzqHAYKIcpPrbTVlSjTnd8+qnHFGavu7NoV337W2iqpKRRUVfpXXFVfY4JCunSOZXbvsYc9Zs6xaKazLdTVVFSAaJXvCmoio3nOvxG3RIprzt2oFLFpU9TvZgx55BPjhD22IDBHgxz9Ovm/DhsCll9oAj717p/YGQRH7DMe9ryUiDBBElL3GjrWb649+FN1n9O2b+r5t2yYfTDKZ+Nfx1iMMEESUvTp3jh31l9KKo7kSEVEoBggiIgrFAEFERKEYIIiIKBQDBBERhWKAICKiUAwQREQUigGCiIhCiQ3Fkf1EZA2AL2pxinYA/pem7GQLXnN+4DXnh5pec3dVbR+2IWcCRG2JSLGqFmU6H3WJ15wfeM35IYprZhUTERGFYoAgIqJQDBC+cZnOQAbwmvMDrzk/pP2a2QZBREShWIIgIqJQDBBERBQq7wOEiAwWkaUislxERmU6P+kiIuNFpFREFgXS2orITBH51Ju38dJFRB72/gYfichhmct5zYlINxF5Q0SWiMhiEbnaS8/Z6xaRQhGZKyIfetd8u5feU0Te867tOREp8NKbeOvLve09MnoBtSAiDUXkAxH5t7ee09csIp+LyEIRWSAixV5apN/tvA4QItIQwFgAJwHoA2CoiNTf9/9Vz1MABseljQLwmqr2AvCatw7Y9ffyppEAHqujPKZbBYBrVbUPgCMAXOH9e+bydW8HcJyqHgqgH4DBInIEgHsAPKCq+wFYD+ASb/9LAKz30h/w9stWVwP4OLCeD9d8rKr2CzzvEO13W1XzdgJwJIAZgfXrAVyf6Xyl8fp6AFgUWF8KoLO33BnAUm/5cQBDw/bL5gnAVAAn5Mt1A2gG4H0Ah8OeqG3kpX/3PQcwA8CR3nIjbz/JdN5rcK1dvRvicQD+DUDy4Jo/B9AuLi3S73ZelyAAdAGwKrC+2kvLVR1V9Wtv+RsAHb3lnPs7eNUI/QG8hxy/bq+qZQGAUgAzAXwGYIOqVni7BK/ru2v2tm8EsGedZjg9HgTwfwB2eet7IvevWQG8IiLzRWSklxbpd7tRTXNK2U1VVURyso+ziOwB4EUAv1bVTSLy3bZcvG5VrQTQT0RaA/gngAMzm6NoicgpAEpVdb6IHJPh7NSlH6hqiYh0ADBTRD4Jboziu53vJYgSAN0C6129tFz1rYh0BgBvXuql58zfQUQaw4LD31V1spec89cNAKq6AcAbsOqV1iLifgAGr+u7a/a2twKwtm5zWmtHAThNRD4HMAlWzfQQcvuaoaol3rwU9kNgICL+bud7gJgHoJfX+6EAwBAA0zKcpyhNAzDcWx4Oq6N36Rd6PR+OALAxUGzNGmJFhb8A+FhV/xjYlLPXLSLtvZIDRKQprM3lY1igOMfbLf6a3d/iHACvq1dJnS1U9XpV7aqqPWD/Z19X1fORw9csIs1FpIVbBjAIwCJE/d3OdMNLpicAJwNYBqu3vTHT+UnjdU0E8DWAnbD6x0tg9a6vAfgUwKsA2nr7Cqw312cAFgIoynT+a3jNP4DV034EYIE3nZzL1w3gEAAfeNe8CMAtXvo+AOYCWA7gHwCaeOmF3vpyb/s+mb6GWl7/MQD+nevX7F3bh9602N2rov5uc6gNIiIKle9VTERElAQDBBERhWKAICKiUAwQREQUigGCiIhCMUAQEVEoBggiIgr1/+TOiaAwjchWAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] From e9c26d3ca16a4817ef5705b1d26a0176e8e77cb4 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Wed, 16 Feb 2022 06:30:59 +0000 Subject: [PATCH 25/27] Sweep is outside --- ...vantage_in_learning_from_experiments.ipynb | 187 +++++++++--------- 1 file changed, 91 insertions(+), 96 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index ae20ef9ec..c092f1b43 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -150,6 +150,7 @@ "def un_bell_pair_block(qubits):\n", " return [cirq.CNOT(qubits[0], qubits[1]), cirq.H(qubits[0])]\n", "\n", + "\n", "def inv_z_basis_gate(pauli):\n", " if pauli == \"I\" or pauli == \"Z\":\n", " return cirq.I\n", @@ -157,35 +158,27 @@ " return cirq.H\n", " if pauli == \"Y\":\n", " # S^dag H to get to computational, H S to go back.\n", - " return cirq.PhasedXZGate(\n", - " axis_phase_exponent=-0.5, x_exponent=0.5, z_exponent=-0.5\n", - " )\n", + " return cirq.PhasedXZGate(axis_phase_exponent=-0.5,\n", + " x_exponent=0.5,\n", + " z_exponent=-0.5)\n", " raise ValueError(\"Invalid Pauli.\")\n", - " \n", - "def build_circuit(\n", - " qubit_pairs,\n", - " pauli,\n", - " flip_params,\n", - " classical_shadows):\n", + "\n", + "\n", + "def build_circuit(qubit_pairs, pauli, classical_shadows):\n", " a_qubits = [pair[0] for pair in qubit_pairs]\n", " b_qubits = [pair[1] for pair in qubit_pairs]\n", " all_qubits = np.concatenate(qubit_pairs)\n", "\n", - " # Add X flips.\n", - " ret_circuit = cirq.Circuit(cirq.X(q) ** p for q, p in zip(all_qubits, flip_params))\n", + " ret_circuit = cirq.Circuit()\n", "\n", " # Add basis turns a and b.\n", - " ret_circuit += [\n", - " inv_z_basis_gate(p)(q) for q, p in zip(a_qubits, pauli)\n", - " ]\n", - " ret_circuit += [\n", - " inv_z_basis_gate(p)(q) for q, p in zip(b_qubits, pauli)\n", - " ]\n", - " \n", + " ret_circuit += [inv_z_basis_gate(p)(q) for q, p in zip(a_qubits, pauli)]\n", + " ret_circuit += [inv_z_basis_gate(p)(q) for q, p in zip(b_qubits, pauli)]\n", + "\n", " if classical_shadows:\n", " # Add measurements.\n", " for i, qubit in enumerate(a_qubits):\n", - " ret_circuit += cirq.measure(qubit, key=f\"q{i}\") \n", + " ret_circuit += cirq.measure(qubit, key=f\"q{i}\")\n", " else: # not classical_shadows\n", " # Add un-bell pair.\n", " ret_circuit += [un_bell_pair_block(pair) for pair in qubit_pairs]\n", @@ -194,7 +187,7 @@ " for i, qubit in enumerate(all_qubits):\n", " ret_circuit += cirq.measure(qubit, key=f\"q{i}\")\n", "\n", - " return ret_circuit\n" + " return ret_circuit" ] }, { @@ -210,34 +203,29 @@ "metadata": {}, "outputs": [], "source": [ - "def create_randomized_sweeps(\n", - " hidden_p,\n", - " symbols,\n", - " n_params,\n", - " rand_state):\n", + "def create_randomized_sweep(hidden_p, system_pairs, rand_source):\n", " last_i = 0\n", " for i, pauli in enumerate(hidden_p):\n", " if pauli != \"I\":\n", " last_i = i\n", "\n", - " sign_p = rand_state.choice([1, -1])\n", - " all_sweeps = []\n", - " for _ in range(n_params):\n", - " current_sweep = dict()\n", - " for twocopy in [0, 1]:\n", - " parity = sign_p * rand_state.choice([1, -1], p=[0.95, 0.05])\n", - " for i, pauli in enumerate(hidden_p):\n", - " current_symbol = symbols[2 * i + twocopy]\n", - " current_sweep[current_symbol] = rand_state.choice([0, 1])\n", - " if pauli != \"I\":\n", - " if last_i == i:\n", - " v = 1 if parity == -1 else 0\n", - " current_sweep[current_symbol] = v\n", - " elif current_sweep[current_symbol] == 1:\n", - " parity *= -1\n", - "\n", - " all_sweeps.append(current_sweep)\n", - " return all_sweeps\n" + " sign_p = rand_source.choice([1, -1])\n", + "\n", + " current_sweep = cirq.Circuit()\n", + " for twocopy in [0, 1]:\n", + " parity = sign_p * rand_source.choice([1, -1], p=[0.95, 0.05])\n", + " for i, pauli in enumerate(hidden_p):\n", + " current_flip = rand_source.choice([0, 1])\n", + " if pauli != \"I\":\n", + " if last_i == i:\n", + " v = 1 if parity == -1 else 0\n", + " current_flip = v\n", + " elif current_flip == 1:\n", + " parity *= -1\n", + " if current_flip == 1:\n", + " current_sweep.append(cirq.X(system_pairs[i][twocopy]))\n", + "\n", + " return current_sweep" ] }, { @@ -271,7 +259,7 @@ " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", "\n", "paulis = []\n", - "for pauli_num in rand_source.choice(range(4 ** n), n_paulis, replace=False):\n", + "for pauli_num in rand_source.choice(range(4**n), n_paulis, replace=False):\n", " pauli = ''\n", " for _ in range(n):\n", " base4 = pauli_num % 4\n", @@ -286,36 +274,23 @@ " pauli_num = (pauli_num - base4) // 4\n", " paulis.append(pauli)\n", "\n", - " flip_params = sympy.symbols(f\"param_0:{len(system_pairs) * 2}\")\n", - " \n", - " circuit = build_circuit(\n", - " system_pairs,\n", - " pauli,\n", - " flip_params,\n", - " classical_shadows=classical_shadows)\n", - " \n", + " base_circuit = build_circuit(system_pairs,\n", + " pauli,\n", + " classical_shadows=classical_shadows)\n", + "\n", + " results_for_pauli = []\n", + "\n", " # Create randomized flippings. These flippings will contain values of 1,0.\n", " # which will turn the X gates on or off.\n", - " sweeps = create_randomized_sweeps(\n", - " pauli, flip_params, n_shots, rand_source\n", - " )\n", - " \n", - " results_for_pauli = []\n", - " for _ in range(n_repeats):\n", - " results_for_repeat = []\n", - " results = simulator.run_sweep(\n", - " program=circuit,\n", - " params=sweeps,\n", - " repetitions=1\n", - " )\n", - "\n", - " batch_results = []\n", - " for j, single_circuit_samples in enumerate(results):\n", - " out0 = single_circuit_samples.data[qubit_order].to_numpy()\n", - " batch_results.append(np.squeeze(out0))\n", - "\n", - " results_for_pauli.append(np.array(batch_results))\n", - " all_results.append(results_for_pauli)" + " for _ in range(n_shots):\n", + " rot_circuit = create_randomized_sweep(pauli, system_pairs, rand_source)\n", + "\n", + " results = simulator.run(program=(rot_circuit + base_circuit),\n", + " repetitions=n_repeats)\n", + " results_for_pauli.append(results.data.to_numpy())\n", + " all_results.append(results_for_pauli)\n", + "\n", + "all_results = np.array(all_results)" ] }, { @@ -339,13 +314,20 @@ "outputs": [], "source": [ "class InnerLayer(tf.keras.Model):\n", + "\n", " def __init__(self, n_shots, num_qubits):\n", " super(InnerLayer, self).__init__(name='inner')\n", " self.n_shots = n_shots\n", " self.num_qubits = num_qubits\n", - " self.gru1 = tf.keras.layers.GRU(4, go_backwards=False, return_sequences=True)\n", - " self.gru2 = tf.keras.layers.GRU(4, go_backwards=True, return_sequences=True)\n", - " self.gru3 = tf.keras.layers.GRU(4, go_backwards=False, return_sequences=False)\n", + " self.gru1 = tf.keras.layers.GRU(4,\n", + " go_backwards=False,\n", + " return_sequences=True)\n", + " self.gru2 = tf.keras.layers.GRU(4,\n", + " go_backwards=True,\n", + " return_sequences=True)\n", + " self.gru3 = tf.keras.layers.GRU(4,\n", + " go_backwards=False,\n", + " return_sequences=False)\n", "\n", " def call(self, x):\n", " x = tf.expand_dims(tf.reshape(x, (-1, self.num_qubits)), -1)\n", @@ -355,18 +337,21 @@ " x = tf.reshape(x, (-1, self.n_shots, 4))\n", " return x\n", "\n", + "\n", "class IntermediateLayer(tf.keras.Model):\n", + "\n", " def __init__(self):\n", " super(IntermediateLayer, self).__init__(name='intermediate')\n", - " \n", + "\n", " def build(self, input_shape):\n", " self.kernel = self.add_weight(\"kernel\", shape=[int(input_shape[2]), 8])\n", - " \n", + "\n", " def call(self, x):\n", " x = tf.math.reduce_mean(x, axis=1)\n", - " x = tf.matmul(x, self.kernel) \n", + " x = tf.matmul(x, self.kernel)\n", " return x\n", "\n", + "\n", "model = tf.keras.Sequential()\n", "model.add(InnerLayer(n_shots, len(qubit_order)))\n", "model.add(IntermediateLayer())" @@ -385,22 +370,31 @@ "metadata": {}, "outputs": [], "source": [ - "input_1 = tf.keras.Input((n_shots, len(qubit_order),))\n", - "input_2 = tf.keras.Input((n_shots, len(qubit_order),))\n", + "input_1 = tf.keras.Input((\n", + " n_shots,\n", + " len(qubit_order),\n", + "))\n", + "input_2 = tf.keras.Input((\n", + " n_shots,\n", + " len(qubit_order),\n", + "))\n", "\n", "encoded_1 = model(input_1)\n", "encoded_2 = model(input_2)\n", "\n", + "\n", "class OuterLayer(tf.keras.Model):\n", + "\n", " def __init__(self):\n", " super(OuterLayer, self).__init__(name='')\n", - " \n", + "\n", " def call(self, x):\n", " x = tf.norm(x[1] - x[0], ord=2, axis=1)\n", " x = tf.stack([x, tf.ones(tf.shape(x))], axis=1)\n", " x = tf.nn.softmax(x)\n", " return x\n", "\n", + "\n", "predictor = OuterLayer()\n", "prediction = predictor([encoded_1, encoded_2])\n", "\n", @@ -425,12 +419,14 @@ "\n", "conjoined_net.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n", "\n", + "\n", "def _sample_different(max_val, ref):\n", " ret = ref\n", " while ret == ref:\n", " ret = rand_source.choice(max_val)\n", " return ret\n", "\n", + "\n", "x1 = []\n", "x2 = []\n", "y = []\n", @@ -438,29 +434,28 @@ " # Same Pauli\n", " for i in range(n_repeats):\n", " j = _sample_different(n_repeats, i)\n", - " \n", - " x1.append(all_results[pauli_idx][i].astype(float))\n", - " x2.append(all_results[pauli_idx][j].astype(float))\n", + "\n", + " x1.append(all_results[pauli_idx, :, i, :].astype(float))\n", + " x2.append(all_results[pauli_idx, :, j, :].astype(float))\n", " y.append([1.0, 0.0])\n", - " \n", + "\n", " # Different Pauli\n", " for i in range(n_repeats):\n", " other_pauli_idx = _sample_different(n_paulis, pauli_idx)\n", " j = rand_source.choice(n_repeats)\n", - " x1.append(all_results[pauli_idx][i].astype(float))\n", - " x2.append(all_results[other_pauli_idx][j].astype(float))\n", + " x1.append(all_results[pauli_idx, :, i, :].astype(float))\n", + " x2.append(all_results[other_pauli_idx, :, j, :].astype(float))\n", " y.append([0.0, 1.0])\n", - " \n", + "\n", "x1 = np.stack(x1)\n", "x2 = np.stack(x2)\n", "y = np.stack(y)\n", "\n", - "history = conjoined_net.fit(\n", - " x=[x1, x2],\n", - " y=y,\n", - " epochs=500,\n", - " batch_size=(2*n_paulis),\n", - " verbose=0)\n" + "history = conjoined_net.fit(x=[x1, x2],\n", + " y=y,\n", + " epochs=500,\n", + " batch_size=(2 * n_paulis),\n", + " verbose=0)" ] }, { @@ -477,7 +472,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] From 12ba40ec8626e60cd2c366a1a14a42c0f6b7a7a1 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Tue, 22 Feb 2022 19:48:21 +0000 Subject: [PATCH 26/27] Use MPS simulator --- ...vantage_in_learning_from_experiments.ipynb | 49 +++++++++++-------- 1 file changed, 29 insertions(+), 20 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index c092f1b43..27d2370d1 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -124,7 +124,8 @@ "import cirq\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import sympy" + "from tensorflow_quantum.core.ops.math_ops import simulate_mps\n", + "from tensorflow_quantum.python import util" ] }, { @@ -151,17 +152,18 @@ " return [cirq.CNOT(qubits[0], qubits[1]), cirq.H(qubits[0])]\n", "\n", "\n", - "def inv_z_basis_gate(pauli):\n", + "def inv_z_basis_gate(circuit, pauli, qubit):\n", " if pauli == \"I\" or pauli == \"Z\":\n", - " return cirq.I\n", - " if pauli == \"X\":\n", - " return cirq.H\n", - " if pauli == \"Y\":\n", - " # S^dag H to get to computational, H S to go back.\n", - " return cirq.PhasedXZGate(axis_phase_exponent=-0.5,\n", - " x_exponent=0.5,\n", - " z_exponent=-0.5)\n", - " raise ValueError(\"Invalid Pauli.\")\n", + " circuit += cirq.I(qubit)\n", + " elif pauli == \"X\":\n", + " circuit += cirq.H(qubit)\n", + " elif pauli == \"Y\":\n", + " # S^dag H to get to computational, H S to go back. \n", + " circuit += cirq.ZPowGate(exponent=0.5)(qubit)\n", + " circuit += cirq.XPowGate(exponent=0.5)(qubit)\n", + " circuit += cirq.ZPowGate(exponent=1.0)(qubit)\n", + " else:\n", + " raise ValueError(\"Invalid Pauli.\")\n", "\n", "\n", "def build_circuit(qubit_pairs, pauli, classical_shadows):\n", @@ -172,8 +174,10 @@ " ret_circuit = cirq.Circuit()\n", "\n", " # Add basis turns a and b.\n", - " ret_circuit += [inv_z_basis_gate(p)(q) for q, p in zip(a_qubits, pauli)]\n", - " ret_circuit += [inv_z_basis_gate(p)(q) for q, p in zip(b_qubits, pauli)]\n", + " for q, p in zip(a_qubits, pauli):\n", + " inv_z_basis_gate(ret_circuit, p, q)\n", + " for q, p in zip(b_qubits, pauli):\n", + " inv_z_basis_gate(ret_circuit, p, q)\n", "\n", " if classical_shadows:\n", " # Add measurements.\n", @@ -248,7 +252,7 @@ "n_repeats = 13\n", "classical_shadows = False\n", "\n", - "system_pairs = [(cirq.GridQubit(0, i), cirq.GridQubit(1, i)) for i in range(n)]\n", + "system_pairs = [(cirq.GridQubit(i, 0), cirq.GridQubit(i, 1)) for i in range(n)]\n", "simulator = cirq.Simulator()\n", "\n", "all_results = []\n", @@ -284,10 +288,15 @@ " # which will turn the X gates on or off.\n", " for _ in range(n_shots):\n", " rot_circuit = create_randomized_sweep(pauli, system_pairs, rand_source)\n", - "\n", - " results = simulator.run(program=(rot_circuit + base_circuit),\n", - " repetitions=n_repeats)\n", - " results_for_pauli.append(results.data.to_numpy())\n", + " \n", + " results = simulate_mps.mps_1d_sample(\n", + " programs=util.convert_to_tensor(\n", + " [rot_circuit + base_circuit]),\n", + " symbol_names=[], symbol_values=[[]],\n", + " num_samples=[n_repeats],\n", + " bond_dim=16)\n", + "\n", + " results_for_pauli.append(np.squeeze(results.numpy()))\n", " all_results.append(results_for_pauli)\n", "\n", "all_results = np.array(all_results)" @@ -472,7 +481,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] From f6eb50d1f2b67e6fb7caf828795aa21a442bc5b3 Mon Sep 17 00:00:00 2001 From: Tony Bruguier Date: Wed, 23 Feb 2022 01:19:23 +0000 Subject: [PATCH 27/27] Now with more descriptive text --- ...vantage_in_learning_from_experiments.ipynb | 337 +++++++++++++++--- 1 file changed, 293 insertions(+), 44 deletions(-) diff --git a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb index 27d2370d1..a43774515 100644 --- a/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb +++ b/docs/tutorials/quantum_advantage_in_learning_from_experiments.ipynb @@ -122,6 +122,7 @@ "source": [ "import tensorflow as tf\n", "import cirq\n", + "from cirq.contrib.svg import SVGCircuit\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from tensorflow_quantum.core.ops.math_ops import simulate_mps\n", @@ -132,14 +133,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Creating the circuit" + "## 1. Creating the circuits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We first define the circuit we are going to use to generate samples" + "We first define the circuit we are going to use to generate samples. We first define the rotations for the Pauli Measurements." ] }, { @@ -148,10 +149,6 @@ "metadata": {}, "outputs": [], "source": [ - "def un_bell_pair_block(qubits):\n", - " return [cirq.CNOT(qubits[0], qubits[1]), cirq.H(qubits[0])]\n", - "\n", - "\n", "def inv_z_basis_gate(circuit, pauli, qubit):\n", " if pauli == \"I\" or pauli == \"Z\":\n", " circuit += cirq.I(qubit)\n", @@ -163,9 +160,39 @@ " circuit += cirq.XPowGate(exponent=0.5)(qubit)\n", " circuit += cirq.ZPowGate(exponent=1.0)(qubit)\n", " else:\n", - " raise ValueError(\"Invalid Pauli.\")\n", - "\n", - "\n", + " raise ValueError(\"Invalid Pauli.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tutorial compares the use of Bell pairs versus not using them (and thus using the previously discovered classical shadows). The point of the paper is that the proposed approach of using Bell pairs is more accurate." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def un_bell_pair_block(qubits):\n", + " return [cirq.CNOT(qubits[0], qubits[1]), cirq.H(qubits[0])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then build the circuit. For simplicity, in case we use classical shadows, we only have half the circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ "def build_circuit(qubit_pairs, pauli, classical_shadows):\n", " a_qubits = [pair[0] for pair in qubit_pairs]\n", " b_qubits = [pair[1] for pair in qubit_pairs]\n", @@ -176,8 +203,9 @@ " # Add basis turns a and b.\n", " for q, p in zip(a_qubits, pauli):\n", " inv_z_basis_gate(ret_circuit, p, q)\n", - " for q, p in zip(b_qubits, pauli):\n", - " inv_z_basis_gate(ret_circuit, p, q)\n", + " if not classical_shadows:\n", + " for q, p in zip(b_qubits, pauli):\n", + " inv_z_basis_gate(ret_circuit, p, q)\n", "\n", " if classical_shadows:\n", " # Add measurements.\n", @@ -191,19 +219,135 @@ " for i, qubit in enumerate(all_qubits):\n", " ret_circuit += cirq.measure(qubit, key=f\"q{i}\")\n", "\n", - " return ret_circuit" + " return ret_circuit\n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We then generate the sweeping parameters." + "Let us now show some examples of circuits for various Paulis and for both classical shadows and Bell measurements." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "q0: q1: IIXHMM" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "_example_qubits = [(cirq.NamedQubit('q0'), cirq.NamedQubit('q1'))]\n", + "\n", + "SVGCircuit(build_circuit(_example_qubits, 'I', False))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "q0: q1: HHXHMM" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SVGCircuit(build_circuit(_example_qubits, 'X', False))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "q0: q1: SSX^0.5X^0.5ZZXHMM" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SVGCircuit(build_circuit(_example_qubits, 'Y', False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case we do use classical shadows, we only see a single qubit:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "q0: SX^0.5ZM" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SVGCircuit(build_circuit(_example_qubits, 'Y', True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then generate the sweeping parameters, following section A.2.a of the paper." + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -232,6 +376,36 @@ " return current_sweep" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us look at an example of a rotation circuit." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "q0: q1: XX" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SVGCircuit(create_randomized_sweep('X', _example_qubits, np.random.RandomState(19950610)))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -239,15 +413,22 @@ "## 2. Create the training data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first define some constants. Some numbers are chosen to be prime for ease of interpreting the dimenstions. Also, we define the qubits to use. " + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "rand_source = np.random.RandomState(20160913)\n", "n_paulis = 3\n", - "n = 3\n", + "n = 4\n", "n_shots = 11\n", "n_repeats = 13\n", "classical_shadows = False\n", @@ -260,10 +441,23 @@ "if classical_shadows:\n", " qubit_order = [f\"q{i}\" for i in range(n)]\n", "else: # not classical_shadows\n", - " qubit_order = [f\"q{i}\" for i in range(2 * n)]\n", - "\n", - "paulis = []\n", - "for pauli_num in rand_source.choice(range(4**n), n_paulis, replace=False):\n", + " qubit_order = [f\"q{i}\" for i in range(2 * n)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define a helper function to convert an integer to a Pauli. The reason is we want to guarantee unique Paulis." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def integer_to_pauli(n, pauli_num):\n", " pauli = ''\n", " for _ in range(n):\n", " base4 = pauli_num % 4\n", @@ -276,6 +470,33 @@ " else:\n", " pauli += 'Z'\n", " pauli_num = (pauli_num - base4) // 4\n", + " return pauli" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then generate all the Paulis with multiple shots." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 11, 13, 8)\n" + ] + } + ], + "source": [ + "paulis = []\n", + "for pauli_num in rand_source.choice(range(4**n), n_paulis, replace=False):\n", + " pauli = integer_to_pauli(n, pauli_num)\n", " paulis.append(pauli)\n", "\n", " base_circuit = build_circuit(system_pairs,\n", @@ -299,7 +520,9 @@ " results_for_pauli.append(np.squeeze(results.numpy()))\n", " all_results.append(results_for_pauli)\n", "\n", - "all_results = np.array(all_results)" + "all_results = np.array(all_results)\n", + "\n", + "print(all_results.shape)" ] }, { @@ -313,12 +536,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First, we create the model that encodes the measurements." + "First, we create the model that encodes the measurements. The first model is a recurrent model (GRU) that encodes along the measurements." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -344,9 +567,22 @@ " x = self.gru2(x)\n", " x = self.gru3(x)\n", " x = tf.reshape(x, (-1, self.n_shots, 4))\n", - " return x\n", - "\n", - "\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we define an intermediate model that summarizes the output of the recurrent model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ "class IntermediateLayer(tf.keras.Model):\n", "\n", " def __init__(self):\n", @@ -370,12 +606,37 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We define the conjoined model that compares outputs" + "Then, we define an outer layer whose role is to compare the two outputs and create a softmax output of dimension 2 predicting whether the two Paulis are indentical or not." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "class OuterLayer(tf.keras.Model):\n", + "\n", + " def __init__(self):\n", + " super(OuterLayer, self).__init__(name='')\n", + "\n", + " def call(self, x):\n", + " x = tf.norm(x[1] - x[0], ord=2, axis=1)\n", + " x = tf.stack([x, tf.ones(tf.shape(x))], axis=1)\n", + " x = tf.nn.softmax(x)\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we define the conjoined model that compares outputs. It uses two inputs, fed through the individual models, and then we " + ] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -392,18 +653,6 @@ "encoded_2 = model(input_2)\n", "\n", "\n", - "class OuterLayer(tf.keras.Model):\n", - "\n", - " def __init__(self):\n", - " super(OuterLayer, self).__init__(name='')\n", - "\n", - " def call(self, x):\n", - " x = tf.norm(x[1] - x[0], ord=2, axis=1)\n", - " x = tf.stack([x, tf.ones(tf.shape(x))], axis=1)\n", - " x = tf.nn.softmax(x)\n", - " return x\n", - "\n", - "\n", "predictor = OuterLayer()\n", "prediction = predictor([encoded_1, encoded_2])\n", "\n", @@ -419,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -476,12 +725,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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