qml.devices.qubit.adjoint_jacobian¶
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adjoint_jacobian(tape, state=None)[source]¶ Implements the adjoint method outlined in Jones and Gacon to differentiate an input tape.
After a forward pass, the circuit is reversed by iteratively applying adjoint gates to scan backwards through the circuit.
Note
The adjoint differentiation method has the following restrictions:
Cannot differentiate with respect to observables.
Cannot differentiate with respect to state-prep operations.
Observable being measured must have a matrix.
- Parameters
tape (QuantumTape) – circuit that the function takes the gradient of
state (TensorLike) – the final state of the circuit; if not provided, the final state will be computed by executing the tape
- Returns
the derivative of the tape with respect to trainable parameters. Dimensions are
(len(observables), len(trainable_params)).- Return type
array or tuple[array]