IQX Colors Big Red-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Circuit library\n",
    "\n",
    "They are all rendering as block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1645.78x686.28 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit import QuantumCircuit\n",
    "\n",
    "circuit = QuantumCircuit(5)\n",
    "circuit.i(0)\n",
    "circuit.x(1)\n",
    "circuit.y(2)\n",
    "circuit.z(3)\n",
    "\n",
    "circuit.h(0)\n",
    "circuit.s(1)\n",
    "circuit.sdg(2)\n",
    "circuit.cx(0, 1)\n",
    "circuit.cy(2, 3)\n",
    "circuit.cz(1, 2)\n",
    "circuit.swap(2, 3)\n",
    "circuit.iswap(2, 3)\n",
    "circuit.dcx(0, 1)\n",
    "\n",
    "circuit.u1(0, 0)\n",
    "circuit.u2(0, 0, 1)\n",
    "circuit.u3(0, 0, 0, 2)\n",
    "\n",
    "\n",
    "circuit.cu1(0, 0, 1)\n",
    "circuit.cu3(0, 0, 0, 2, 3)\n",
    "\n",
    "circuit.ch(3, 4)\n",
    "\n",
    "circuit.rx(0, 0)\n",
    "circuit.ry(0, 1)\n",
    "circuit.rz(0, 2)\n",
    "circuit.r(0, 0, 3)\n",
    "circuit.t(4)\n",
    "circuit.tdg(4)\n",
    "\n",
    "circuit.rxx(0, 0, 1)\n",
    "circuit.ryy(0, 1, 2)\n",
    "circuit.rzz(0, 2, 3)\n",
    "circuit.rzx(0, 0, 1)\n",
    "circuit.ccx(0, 1, 2)\n",
    "circuit.cswap(0, 1, 2)\n",
    "circuit.rccx(0, 1, 2)\n",
    "circuit.rcccx(0, 1, 2, 3)\n",
    "circuit.ms([[0] * 5] * 5, [0, 1, 2, 3, 4])\n",
    "\n",
    "circuit.unitary(np.eye(4), [0, 1])\n",
    "circuit.initialize(np.eye(4)[0,:], [2, 3])\n",
    "circuit.isometry(np.eye(4), [], [1, 2])\n",
    "\n",
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1164.18x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import QFT  # e.g. for the Quantum Fourier Transform\n",
    "QFT(4).draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 392.767x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import OR\n",
    "OR(4).draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 491.969x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import LinearPauliRotations\n",
    "LinearPauliRotations(4, offset=2, slope=0.5).draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 635.649x565.88 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import PiecewiseLinearPauliRotations\n",
    "PiecewiseLinearPauliRotations(4, breakpoints=[0, 2], offsets=[2, 1], slopes=[0.5, -0.5]).draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 983.577x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import QuantumVolume\n",
    "QuantumVolume(4).decompose().draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1645.78x866.88 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QuantumVolume(4).decompose().decompose().draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 983.577x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import EfficientSU2\n",
    "EfficientSU2(3, entanglement='linear').draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1704.91x1167.88 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import WeightedAdder\n",
    "WeightedAdder(3, [1,2,1]).draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 580.436x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import IntegerComparator\n",
    "IntegerComparator(3, 4).draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 682.577x445.48 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "circuit = QuantumCircuit(7)\n",
    "circuit.mcx([0, 1, 2, 3], 4, ancilla_qubits=[5, 6], mode='v-chain')\n",
    "circuit.decompose().draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1645.78x927.08 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.decompose().decompose().draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1645.78x686.28 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import ZZFeatureMap\n",
    "ZZFeatureMap(3).draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1465.18x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import ExcitationPreserving\n",
    "ExcitationPreserving(3).draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 682.577x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import IQP\n",
    "IQP([[6, 5, 3], [5, 4, 5], [3, 5, 1]]).decompose().draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 742.777x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import GMS\n",
    "GMS(5, [[0.2] * 5] * 5).decompose().draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1645.78x686.28 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GMS(5, [[0.2] * 5] * 5).decompose().decompose().draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit import QuantumCircuit\n",
    "from qiskit.aqua.operators import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Pauli'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op = (0.2 * (X ^ Y ^ Z) + 3 * (I ^ Z ^ I))\n",
    "op.primitive_strings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = PauliExpectation().convert(~StateFn(op) @ state)\n",
    "sampler = CircuitSampler(backend).convert(exp)\n",
    "result = sampler.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = PauliExpectation().convert(~StateFn(op) @ state)\n",
    "result = exp.eval(backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = MatrixOp([[1, 0], [0, -1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "op = (z + Z + z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SummedOp([MatrixOp(Operator([[ 1.+0.j,  0.+0.j],\n",
       "          [ 0.+0.j, -1.+0.j]],\n",
       "         input_dims=(2,), output_dims=(2,)), coeff=2.0), PauliOp(Pauli(z=[True], x=[False]), coeff=1.0)], coeff=1.0, abelian=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.reduce()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((z + Z) @ (Z + z)) == ((z + Z) @ (z + Z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkProcess-2:\n",
      "Process ForkProcess-1:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/process.py\", line 233, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/process.py\", line 233, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 93, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 94, in get\n",
      "    res = self._recv_bytes()\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "KeyboardInterrupt\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "can't pickle PyCapsule objects",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31m_RemoteTraceback\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\", line 236, in _feed\n    obj = _ForkingPickler.dumps(obj)\n  File \"/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/reduction.py\", line 51, in dumps\n    cls(buf, protocol).dump(obj)\nTypeError: can't pickle PyCapsule objects\n\"\"\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-6bcb6c324f75>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mlist_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mListOp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mexpectation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPauliExpectation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mStateFn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_op\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCircuitSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'statevector_simulator'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpectation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/operators/converters/circuit_sampler.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self, operator, params)\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0mcircs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_circuit_ops_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transpiled_circ_cache\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         sampled_statefn_dicts = self.sample_circuits(circuit_sfns=circs,\n\u001b[0;32m--> 194\u001b[0;31m                                                      param_bindings=param_bindings)\n\u001b[0m\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mreplace_circuits_with_dicts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/operators/converters/circuit_sampler.py\u001b[0m in \u001b[0;36msample_circuits\u001b[0;34m(self, circuit_sfns, param_bindings)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         results = self.quantum_instance.execute(ready_circs,\n\u001b[0;32m--> 272\u001b[0;31m                                                 had_transpiled=self._transpile_before_bind)\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m         \u001b[0;31m# Wipe parameterizations, if any\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/quantum_instance.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, circuits, had_transpiled)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0;31m# maybe compile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhad_transpiled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m             \u001b[0mcircuits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m         \u001b[0;31m# assemble\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Work/Qiskit/qiskit-aqua/qiskit/aqua/quantum_instance.py\u001b[0m in \u001b[0;36mtranspile\u001b[0;34m(self, circuits)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \"\"\"\n\u001b[1;32m    233\u001b[0m         transpiled_circuits = compiler.transpile(circuits, self._backend, **self._backend_config,\n\u001b[0;32m--> 234\u001b[0;31m                                                  **self._compile_config)\n\u001b[0m\u001b[1;32m    235\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtranspiled_circuits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0mtranspiled_circuits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtranspiled_circuits\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/compiler/transpile.py\u001b[0m in \u001b[0;36mtranspile\u001b[0;34m(circuits, backend, basis_gates, coupling_map, backend_properties, initial_layout, layout_method, routing_method, translation_method, seed_transpiler, optimization_level, pass_manager, callback, output_name)\u001b[0m\n\u001b[1;32m    217\u001b[0m     \u001b[0;31m# Transpile circuits in parallel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)),\n\u001b[0;32m--> 219\u001b[0;31m                             task_args=(faulty_qubits_map, backend))\n\u001b[0m\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuits\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/tools/parallel.py\u001b[0m in \u001b[0;36mparallel_map\u001b[0;34m(task, values, task_args, task_kwargs, num_processes)\u001b[0m\n\u001b[1;32m    135\u001b[0m             \u001b[0;31m# Otherwise just reset parallel flag and error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m             \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'QISKIT_IN_PARALLEL'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'FALSE'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0mPublisher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpublish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"terra.parallel.finish\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Work/Qiskit/qiskit-terra/qiskit/tools/parallel.py\u001b[0m in \u001b[0;36mparallel_map\u001b[0;34m(task, values, task_args, task_kwargs, num_processes)\u001b[0m\n\u001b[1;32m    125\u001b[0m                 \u001b[0mfuture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecutor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_task_wrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m             \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m             \u001b[0mPublisher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpublish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"terra.parallel.done\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/process.py\u001b[0m in \u001b[0;36m_chain_from_iterable_of_lists\u001b[0;34m(iterable)\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0mcareful\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mto\u001b[0m \u001b[0mkeep\u001b[0m \u001b[0mreferences\u001b[0m \u001b[0mto\u001b[0m \u001b[0myielded\u001b[0m \u001b[0mobjects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    482\u001b[0m     \"\"\"\n\u001b[0;32m--> 483\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0melement\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    484\u001b[0m         \u001b[0melement\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult_iterator\u001b[0;34m()\u001b[0m\n\u001b[1;32m    596\u001b[0m                     \u001b[0;31m# Careful not to keep a reference to the popped future\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m                         \u001b[0;32myield\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    599\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_time\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonotonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    426\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    382\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/queues.py\u001b[0m in \u001b[0;36m_feed\u001b[0;34m(buffer, notempty, send_bytes, writelock, close, ignore_epipe, onerror, queue_sem)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m                         \u001b[0;31m# serialize the data before acquiring the lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m                         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ForkingPickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    237\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mwacquire\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m                             \u001b[0msend_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/reduction.py\u001b[0m in \u001b[0;36mdumps\u001b[0;34m(cls, obj, protocol)\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetbuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can't pickle PyCapsule objects"
     ]
    }
   ],
   "source": [
    "from qiskit import Aer\n",
    "from qiskit.aqua.operators import *\n",
    "\n",
    "ops = [X, X ^ X, X ^ 5]\n",
    "state = Zero\n",
    "\n",
    "list_op = ListOp(ops)\n",
    "expectation = PauliExpectation().convert(~StateFn(list_op) @ state)\n",
    "sampler = CircuitSampler(Aer.get_backend('statevector_simulator')).convert(expectation)\n",
    "sampler.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ListOp([MatrixOp(Operator([[0.+0.j, 1.+0.j],\n",
       "          [1.+0.j, 0.+0.j]],\n",
       "         input_dims=(2,), output_dims=(2,)), coeff=1.0), MatrixOp(Operator([[0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],\n",
       "          [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j],\n",
       "          [0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j],\n",
       "          [1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]],\n",
       "         input_dims=(2, 2), output_dims=(2, 2)), coeff=1.0)], coeff=1.0, abelian=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ListOp([X, X^X]).to_matrix_op()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit import Parameter, QuantumCircuit, Instruction\n",
    "from qiskit.circuit.library import MCMT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "th = Parameter('θ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x12b1c2cd0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(5)\n",
    "# qc.mcmt('h', [0, 1], [2, 3])\n",
    "qc.append(MCMT('h', 3, 2, label='gate'), [0, 1, 4, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=292x172 at 0x12B1188D0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.decompose().draw(output='latex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumRegister, transpile\n",
    "from qiskit.circuit.library import RXGate, RYGate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mcrx: OrderedDict([('cx', 92), ('u3', 62), ('u1', 1)])\n",
      "RXGate: OrderedDict([('cx', 92), ('u3', 62), ('u1', 1)])\n"
     ]
    }
   ],
   "source": [
    "num_ctrl = 5\n",
    "q_ctrl = QuantumRegister(num_ctrl)\n",
    "q_target = QuantumRegister(1)\n",
    "function = QuantumCircuit(q_ctrl, q_target)\n",
    "function.mcrx(0.2, q_ctrl[:], q_target[0], None)\n",
    "\n",
    "gate = QuantumCircuit(q_ctrl, q_target)\n",
    "gate.append(RXGate(0.2).control(num_ctrl), q_ctrl[:] + q_target[:])\n",
    "basis = ['u1', 'u3', 'cx']\n",
    "print('mcrx:', transpile(function, basis_gates=basis).count_ops())\n",
    "print('RXGate:', transpile(gate, basis_gates=basis).count_ops())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.5 64-bit ('latest': virtualenv)",
   "language": "python",
   "name": "python37564bitlatestvirtualenv76cfe9d7d3f749489cf2ff69593234af"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}