qml.vn_entropy¶
-
vn_entropy
(wires, log_base=None)[source]¶ Von Neumann entropy of the system prior to measurement.
\[S( \rho ) = -\text{Tr}( \rho \log ( \rho ))\]- Parameters
wires (Sequence[int] or int) – The wires of the subsystem
log_base (float) – Base for the logarithm.
- Returns
Measurement process instance
- Return type
Example:
dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def circuit_entropy(x): qml.IsingXX(x, wires=[0, 1]) return qml.vn_entropy(wires=[0])
Executing this QNode:
>>> circuit_entropy(np.pi/2) 0.6931472
It is also possible to get the gradient of the previous QNode:
>>> param = np.array(np.pi/4, requires_grad=True) >>> qml.grad(circuit_entropy)(param) tensor(0.62322524, requires_grad=True)
Note
Calculating the derivative of
vn_entropy()
is currently supported when using the classical backpropagation differentiation method (diff_method="backprop"
) with a compatible device and finite differences (diff_method="finite-diff"
).
code/api/pennylane.vn_entropy
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