qml.shadows.shadow_expval

shadow_expval(H, k=1)

Transform a QNode returning a classical shadow into one that returns the approximate expectation values in a differentiable manner.

See shadow_expval() for more usage details.

Parameters

• H (Observable or list[Observable]) – Observables for which to compute the expectation values

• k (int) – k (int): Number of equal parts to split the shadow’s measurements to compute the median of means. k=1 corresponds to simply taking the mean over all measurements.

Returns

1-D tensor containing the expectation value estimates for each observable

Return type

tensor-like[float]

Example

H = qml.PauliZ(0) @ qml.PauliZ(1)
dev = qml.device("default.qubit", wires=2, shots=10000)

@qml.shadows.shadow_expval(H, k=1)
@qml.qnode(dev)
def circuit(x):
    qml.Hadamard(wires=0)
    qml.CNOT(wires=[0, 1])
    qml.RX(x, wires=0)
    return qml.classical_shadow(wires=[0, 1])

>>> x = np.array(1.2)
>>> circuit(x)
tensor(0.3069, requires_grad=True)
>>> qml.grad(circuit)(x)
-0.9323999999999998

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