quantum_fisher transform on non-default.qubit devices

[albi3ro]

Context:

The qml.qinfo.quantum_fisher transform was failing on non-defualt.qubit devices:

import pennylane as qml
from pennylane import numpy as np

from sklearn import datasets as ds

NUM_WIRES = 4

def get_circuit(data, parameters):
    qml.IQPEmbedding(data, wires=range(NUM_WIRES), n_repeats=1)
    for i in range(NUM_WIRES):
        qml.RX(parameters[i], wires=i)
    for j in range(NUM_WIRES-1):
        qml.CNOT(wires=[j, j+1])
    return qml.expval(qml.PauliZ(0))

# use different devices here, i.e. lightning.qubit, default.qubit, lightning.kokkos
dev = qml.device("default.mixed", wires=NUM_WIRES)

def qfim(X_train, parameters):
    circuit = qml.QNode(get_circuit, dev)
    data = np.array(X_train[0], requires_grad=False)
    return qml.qinfo.transforms.quantum_fisher(circuit)(data, parameters)

X = ds.load_iris().data
parameters = np.random.random(size=NUM_WIRES, requires_grad=True)
print(qfim(X, parameters))

Description of the Change:

Use the metric_tensor instead of the adjoint_metric_tensor if the device is not default.qubit.

Benefits:

quantum_fisher works with more devices.

Possible Drawbacks:

Related GitHub Issues:

Fixes #5381 [sc-58882]

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 99.63%. Comparing base (186faaf) to head (ce37e29).

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #5423      +/-   ##
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- Coverage   99.63%   99.63%   -0.01%     
==========================================
  Files         399      399              
  Lines       37112    36825     -287     
==========================================
- Hits        36977    36689     -288     
- Misses        135      136       +1     

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[astralcai]

Looks good