Release Note

New Features

paddle_quantum.ansatz:
- New member depth: return the depth of the circuit.
- New member function transfer_static: make the circuit not trainable.
- New member function collapse: add Collapse operator into the circuit.
paddle_quantum.gate:
- New base gate ParamGate: base class for all parameterized gates, responsible for functions designed for parameterized gates.
- New member gate_name and member function gate_history_generation: by simply defining gate_name or overloading gate_history_generation, now Circuit.gate_history can read the gate history of your self-designed Gates.
- New Gate QAOALayerWeighted: QAOA driving layers with weights.
paddle_quantum.operator:
- New operator Collapse: support (partially) collapse for quantum states.
paddle_quantum.qsvt: new module, providing tools for Chebyshev-based QSP & QSVT.
- New class ScalarQSP: class for circuit and matrix generation in single-qubit QSP.
- New class QSVT: class for circuit and matrix generation in QSVT.
paddle_quantum.state:
- In state_vector backend, class State now can call its member properties State.ket and State.bra corresponding to ket and bra representations of the state.
paddle_quantum.qinfo:
- Now support inputs for both paddle.Tensor and State.
- New function tensor_product: State version of Nkron.
- partial_trace now support the state_vector backend.

If the dtype of input param of ParamGate is

None, then ParamGate will create its own (random) parameter.
ParamBase (generated by paddle.create_parameter), then ParamGate will treat param as its own parameter.
paddle.Tensor but not ParamBase, then ParamGate will treat param as a fixed input, even when param is trainable (i.e. when param.stop_gradient is False).
float or Iterable[float], then ParamGate will treat param as a fixed input.

Add the tutorial Quantum Signal Processing and Quantum Singular Value Transformation, which presents a brief but systematic illustration of QSP and QSVT.

Add the tutorial Data Encoding Analysis, which analyzes the effect of the width and depth of data encoding circuits on quantum states from the view of quantum information.
Add the tutorial Quantum Neural Network Approximating Functions, which demonstrates how to use single-qubit QNNs to approximate any (scalable) square-integrable functions.