qml.devices.qubit.adjoint_vjp

adjoint_vjp(tape, cotangents, state=None)

The vector jacobian product used in reverse-mode differentiation.

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.

• Observable being measured must have a matrix.

Parameters

• tape (QuantumTape) – circuit that the function takes the gradient of

• cotangents (Tuple[Number]) –

  gradient vector for output parameters. For computing the full Jacobian, the cotangents can be batched to vectorize the computation. In this case, the cotangents can have the following shapes. batch_size below refers to the number of entries in the Jacobian:

  • For a state measurement, cotangents must have shape (batch_size, 2 ** n_wires).

  • For n expectation values, the cotangents must have shape (n, batch_size). If n = 1, then the shape must be (batch_size,).

• state (TensorLike) – the final state of the circuit; if not provided, the final state will be computed by executing the tape

Returns

gradient vector for input parameters

Return type

Tuple[Number]

code/api/pennylane.devices.qubit.adjoint_vjp

 

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