Release Note

Improvements

• Accelerated circuit executions, providing 2-4 times speed-up compared to the previous version. Specifically, over 10 times speed-up for specific quantum neural network models.

New Features

• paddle_quantum.gate:
  • Gate is now a child class of paddle_quantum.channel.Channel, and hence inherits most functionalities from the channel module, such as its Choi representation Gate.choi_repr.
  • New module matrix: provides user access to the matrices of gates in Paddle Quantum.
  • New gate ParamOracle: provides user access to customized parameterized gates.
• paddle_quantum.qinfo:
  • New function pauli_str_convertor: Concatenate the input observable with coefficient 1.
• paddle_quantum.loss.ExpecVal:
  • Now ExpecVal.forward() can return the decomposed expectation value by setting decompose=True.
• paddle_quantum.state.State:
  • Now State.measure() can record the result in each shot by setting record=True.

New Applications

New applications have been added in the Quantum Application Model Library (QAML) as follows.

1. Credit Risk Analysis
2. Deuteron Binding Energy
3. Handwritten Digits Generation
4. Intent Classification
5. Power Flow Optimization
6. Random Number Generation

New Tutorials

More tutorials are introduced in Paddle Quantum 2.4.0, to offer suggested usages in common scenarios of quantum research. These tutorials are listed as follows:

1. Construction and Manipulation of Circuit
2. Customized Gate and Channel
3. Generation of Hamiltonian
4. Common Algebraic Functions
5. Usage of State
6. Construction and Training of QNNs

Bug Fixes

• Fix some typos and mistakes in the tutorials and the API docs.
• Strengthen the overall stability of Paddle Quantum.

Dependencies

• paddlenlp: newly added.