quantum-ods / qmlcourse

Курс по квантовому машинному обучению
https://quantum-ods.github.io/qmlcourse/
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dependabot: bump pennylane from 0.20.0 to 0.32.0 #563

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Bumps pennylane from 0.20.0 to 0.32.0.

Release notes

Sourced from pennylane's releases.

Release 0.32.0

  • It is now possible to encode an operator A into a quantum circuit by decomposing it into a linear combination of unitaries using PREP (qml.StatePrep) and SELECT (qml.Select) routines. [(#4431)](PennyLaneAI/pennylane#4431) [(#4437)](PennyLaneAI/pennylane#4437) [(#4444)](PennyLaneAI/pennylane#4444) [(#4450)](PennyLaneAI/pennylane#4450) [(#4506)](PennyLaneAI/pennylane#4506) [(#4526)](PennyLaneAI/pennylane#4526)

    Consider an operator A composed of a linear combination of Pauli terms:

    >>> A = qml.PauliX(2) + 2 * qml.PauliY(2) + 3 * qml.PauliZ(2)
    

    A decomposable block-encoding circuit can be created:

    def block_encode(A, control_wires):
        probs = A.coeffs / np.sum(A.coeffs)
        state = np.pad(np.sqrt(probs, dtype=complex), (0, 1))
        unitaries = A.ops
    
    qml.StatePrep(state, wires=control_wires)
    qml.Select(unitaries, control=control_wires)
    qml.adjoint(qml.StatePrep)(state, wires=control_wires)
    

    >>> print(qml.draw(block_encode, show_matrices=False)(A, control_wires=[0, 1]))
    0: ─╭|Ψ⟩─╭Select─╭|Ψ⟩†─┤
    1: ─╰|Ψ⟩─├Select─╰|Ψ⟩†─┤
    2: ──────╰Select───────┤
    

    This circuit can be used as a building block within a larger QNode to perform algorithms such as QSVT and Hamiltonian simulation.

  • Decomposing a Hermitian matrix into a linear combination of Pauli words via qml.pauli_decompose is now faster and differentiable. [(#4395)](PennyLaneAI/pennylane#4395) [(#4479)](PennyLaneAI/pennylane#4479) [(#4493)](PennyLaneAI/pennylane#4493)

    def find_coeffs(p):
        mat = np.array([[3, p], [p, 3]])
        A = qml.pauli_decompose(mat)
        return A.coeffs
    
    >>> import jax
    >>> from jax import numpy as np
    >>> jax.jacobian(find_coeffs)(np.array(2.))
    Array([0., 1.], dtype=float32, weak_type=True)
    

... (truncated)

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dependabot[bot] commented 1 year ago

OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version. You can also ignore all major, minor, or patch releases for a dependency by adding an ignore condition with the desired update_types to your config file.

If you change your mind, just re-open this PR and I'll resolve any conflicts on it.