qml.mutual_info¶
-
mutual_info(wires0, wires1, log_base=None)[source]¶ Mutual information between the subsystems prior to measurement:
\[I(A, B) = S(\rho^A) + S(\rho^B) - S(\rho^{AB})\]where \(S\) is the von Neumann entropy.
The mutual information is a measure of correlation between two subsystems. More specifically, it quantifies the amount of information obtained about one system by measuring the other system.
- Parameters
wires0 (Sequence[int] or int) – the wires of the first subsystem
wires1 (Sequence[int] or int) – the wires of the second 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_mutual(x): qml.IsingXX(x, wires=[0, 1]) return qml.mutual_info(wires0=[0], wires1=[1])
Executing this QNode:
>>> circuit_mutual(np.pi/2) 1.3862943611198906
It is also possible to get the gradient of the previous QNode:
>>> param = np.array(np.pi/4, requires_grad=True) >>> qml.grad(circuit_mutual)(param) tensor(1.24645048, requires_grad=True)
Note
Calculating the derivative of
mutual_info()is currently supported when using the classical backpropagation differentiation method (diff_method="backprop") with a compatible device and finite differences (diff_method="finite-diff").