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Paddle Quantum 2.3.0
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@Quleaf Quleaf released this 18 Jan 11:19
· 3 commits to master since this release
v2.3.0
2884515

Release Note

New Features

  • paddle_quantum.channel:
    • New module paddle_quantum.channel.representation: A library of representations of quantum channels in Paddle Quantum.
    • New channel GeneralizedDepolarizing and its interface in paddle_quantum.ansatz.Circuit: A generalized depolarizing channel.
    • New member properties choi_oper, kraus_oper, stinespring_oper of Channel: Provide user accesses to the Choi, Kraus and Stinespring representations of class Channel and its subclasses.
  • paddle_quantum.linalg:
    • New function is_positive: Check whether the input matrix is a positive semi-definite matrix.
    • New function pauli_basis_generation: Generate a Pauli basis.
    • New function pauli_decomposition: Decompose the input matrix by the Pauli basis.
    • New function subsystem_decomposition: Decompose the input matrix by two given bases in two subsystems.
  • New module paddle_quantum.model: Provides integrated features such as training of quantum neural networks (QNN), prediction using the trained models, and convenient accesses to the optimizers and the schedulers of Paddle Paddle.
    • OptModel: The class for the optimization-based QNN model.
    • LearningModel: The class for the learning-based QNN model.
    • EncodingModel: The class for the encoding-based QNN model.
  • paddle_quantum.qinfo:
    • New function is_choi: Check whether a quantum operator is a Choi operator.
    • New function permute_systems: Permute the quantum systems based on a permute list.
    • New function channel_repr_convert: Provide transformations among Choi, Kraus and Stinespring representations of a channel. PS: substitute the original function channel_convert.
    • New function random_channel: Generate a random channel.
    • New function grover_generation: Generate a Grover operator based on the oracle given.
    • New function qft_generation: Generate a unitary matrix for the circuit of quantum Fourier transform.

Built-in model library

Paddle Quantum now provides the built-in QNN model and the other quantum algorithms. The users can use these models and algorithms to implement the quantum applications quickly. The built-in models and algorithms are as follows.

  • biocomputing: Provides the protein folding algorithms based on protein lattice model and CVaR loss function.
  • data_analysis: Provides the variational quantum regression (VQR) and the variational quantum linear solver (VQLS) model.
  • finance: Provides the quantum option pricing algorithm and the quantum portfolio optimization algorithm.
  • qchem: Provides native support for molecular construction and interfaces to external quantum chemistry packages (Molecule, PySCFDriver), add more powerful quantum chemistry ansatz (UCC, HartreeFock), provides support for defining fermionic quantum state (WaveFunction), and add more tools for estimating molecular properties (symmetric_rdm1e, dipole_moment).
  • qml: Provides the quantum neural network for medical image classification (QNNMIC), the quantum neural network for quality detection (QNNQD), the quantum self-attention neural network (QSANN), and the variational shadow quantum learning (VSQL) model.

New applications

The Quantum Application Model Library (QAML) is newly added in Paddle Quantum in this release. The applications currently included in QAML are as follows.

  1. Handwritten digits classification
  2. Molecular ground state energy & dipole moment calculation
  3. Text classification
  4. Protein folding
  5. Medical image classification
  6. Quality detection
  7. Option pricing
  8. Quantum portfolio optimization
  9. Regression
  10. Quantum linear equation solver

Bug Fixes

  • Fix some typo and mistakes in the tutorials and the API docs.

Improvements

  • The qchem tutorials has been updated to fit new APIs.

Dependencies

  • imbalanced-learn: newly added.
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