Release Note

New Features

• paddle_quantum.ansatz.Circuit:
  • New member function plot: now we can plot the circuit using matplotlib.
• paddle_quantum.gate:
  • New Gate Sdg: dagger of the S gate
  • New Gate Tdg: dagger of the T gate
• New Gate member gate_info: contains the necessary information for the Gate class. You can revise this member to adjust the appearance of a particular gate class in Circuit.plot.
• paddle_quantum.channel:
  • New Channel MixedUnitaryChannel: a random mixed unitary channel.
  • Kraus operator of the Depolarizing channel is revised for consistency with the representation in QCQI.
  • New Channel ChoiRepr: a general quantum channel under the Choi representation.
• New Channel StringspringRepr: a general quantum channel under the Choi representation.
• paddle_quantum.state:
  • paddle_quantum.state.State:
    • New member function normalize: provide the ability to be self-normalized.
    • New member function evolve: provide the ability of self-evolution for a given Hamiltonian and time.
    • New member function kron: Kronecker product for State class.
  • New function is_state_vector: verify whether the input data is a legal state vector.
  • New function is_density_matrix: verify whether the input data is a legal density matrix.
• New operation @: matrix multiplication for State class (under density_matrix backend).
• paddle_quantum.qpp: new module, providing a systematic set of tools for quantum phase processing. See the corresponding tutorial for more details.
• paddle_quantum.qml: new module that includes models in the domain of QML. Currently it contains the VSQL (Variational Shadow Quantum Learning) model and related functionals.

Improvements

• paddle_quantum.linalg: inputs of functions are now compatible with paddle_quantum.state.State, paddle.Tensorand numpy.ndarray.
• paddle_quantum.qinfo:
  • Inputs of functions are now compatible with paddle_quantum.state.State, paddle.Tensorand numpy.ndarray.
• Rewrite the logic of partial_trace, partial_trace_discontiguous and partial_transpose using tensor contraction, significantly improving the performance of these three functions.

New Tutorials

Introduction

• Add the introduction part for the resolution of version conflict happened when using QuLeaf to connect the quantum computer.

Machine Learning

• Add the tutorial Variational quantum amplitude estimation which implements single-qubit variational quantum amplitude estimation (VQAE).

Quantum Simulation

• Add the tutorial Hamiltonian Simulation with qDRIFT which introduces a random method named quantum stochastic drift protocol (qDRIFT) which is based on product formula.
• Add the tutorial Quantum Phase Processing which provides access to the eigenphases of the target unitary, allowing phase transformation or extraction to be done in an efficient and precise manner.
• Add the tutorial Variational Quantum Metrology which introduces a variational method to search an optimal Ramsey interferometer for estimating the unknown parameters.

Bug Fixes

• Fix the bug in the paddle_quantum.ansatz.vans module caused by the implementation of the parameter gate.
• Fix some typo and mistakes in the tutorials and the API docs.

Dependencies

• cvxpy: newly added.
• rich: newly added.
• scipy: remove the version restrictions.