paddle_quantum.loss.measure

The source file of the class for the measurement.

class paddle_quantum.loss.measure.ExpecVal(hamiltonian, shots=0)

The class of the loss function to compute the expectation value for the observable.

This interface can make you using the expectation value for the observable as the loss function.

Parameters:

hamiltonian (Hamiltonian) – The input observable.
shots (int | None) – The number of measurement shots. Defaults to 0. Now it just need to be input when the backend is QuLeaf.

forward(state, decompose=False)

Compute the expectation value of the observable with respect to the input state.

The value computed by this function can be used as a loss function to optimize.

Parameters:

state (State) – The input state which will be used to compute the expectation value.
decompose (bool | None) – Defaults to False. If decompose is True, it will return the expectation value of each term.

Raises:

NotImplementedError – The backend is wrong or not supported.

Returns:

The expectation value. If the backend is QuLeaf, it is computed by sampling.

Return type:

Tensor | List[Tensor]

class paddle_quantum.loss.measure.Measure(measure_basis='z')

Compute the probability of the specified measurement result.

Parameters:: measure_basis (Iterable[Tensor] | str | None) – Specify the basis of the measurement. Defaults to 'z'.
Raises:: NotImplementedError – Currently we just support the z basis.

forward(state, qubits_idx='full', desired_result=None)

Compute the probability of measurement to the input state.

Parameters:

state (State) – The quantum state to be measured.
qubits_idx (Iterable[int] | int | str | None) – The index of the qubits to be measured. Defaults to 'full' which means measure all the qubits.
desired_result (Iterable[str] | str | None) – Specify the results of the measurement to return. Defaults to None which means return the probability of all the results.

Raises:

Returns:

The probability of the measurement.

Return type:

Tensor