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Baidu (ERNIE) Release: PaddlePaddle/Quantum v2.2.1

published Sep 6, 2022seen 5dcaptured 8hhttp 200method plain

Repository: PaddlePaddle/Quantum

Tag: v2.2.1

Published: 2022-09-06T09:30:15Z

Prerelease: no

Release notes:

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.