# qml.qinfo.transforms.relative_entropy¶

relative_entropy(qnode0, qnode1, wires0, wires1)[source]

Compute the relative entropy for two QNode returning a state() (a state can be a state vector or a density matrix, depending on the device) acting on quantum systems with the same size.

$S(\rho\,\|\,\sigma)=-\text{Tr}(\rho\log\sigma)-S(\rho)=\text{Tr}(\rho\log\rho)-\text{Tr}(\rho\log\sigma) =\text{Tr}(\rho(\log\rho-\log\sigma))$

Roughly speaking, quantum relative entropy is a measure of distinguishability between two quantum states. It is the quantum mechanical analog of relative entropy.

Parameters
Returns

A function that takes as input the joint arguments of the two QNodes, and returns the relative entropy from their output states.

Return type

func

Example

Consider the following QNode:

dev = qml.device('default.qubit', wires=2)

@qml.qnode(dev)
def circuit(param):
qml.RY(param, wires=0)
qml.CNOT(wires=[0, 1])
return qml.state()


The qml.qinfo.relative_entropy transform can be used to compute the relative entropy between the output states of the QNode:

>>> relative_entropy_circuit = qml.qinfo.relative_entropy(circuit, circuit, wires0=, wires1=)


The returned function takes two tuples as input, the first being the arguments to the first QNode and the second being the arguments to the second QNode:

>>> x, y = np.array(0.4), np.array(0.6)
>>> relative_entropy_circuit((x,), (y,))
0.017750012490703237


This transform is fully differentiable:

def wrapper(x, y):
return relative_entropy_circuit((x,), (y,))

>>> wrapper(x, y)
0.017750012490703237