qml.devices.qubit.apply_operation

apply_operation(op, state, is_state_batched=False, debugger=None, **_)

Apply and operator to a given state.

Parameters

• op (Operator) – The operation to apply to state

• state (TensorLike) – The starting state.

• is_state_batched (bool) – Boolean representing whether the state is batched or not

• debugger (_Debugger) – The debugger to use

• **execution_kwargs (Optional[dict]) – Optional keyword arguments needed for applying some operations described below.

Keyword Arguments

• mid_measurements (dict, None) – Mid-circuit measurement dictionary mutated to record the sampled value

• interface (str) – The machine learning interface of the state

• postselect_mode (str) – Configuration for handling shots with mid-circuit measurement postselection. Use "hw-like" to discard invalid shots and "fill-shots" to keep the same number of shots. None by default.

• rng (Optional[numpy.random._generator.Generator]) – A NumPy random number generator.

• prng_key (Optional[jax.random.PRNGKey]) – An optional jax.random.PRNGKey. This is the key to the JAX pseudo random number generator. Only for simulation using JAX. If None, a numpy.random.default_rng will be used for sampling.

• tape_shots (Shots) – the shots object of the tape

Returns

output state

Return type

ndarray

Warning

apply_operation is an internal function, and thus subject to change without a deprecation cycle.

Warning

apply_operation applies no validation to its inputs.

This function assumes that the wires of the operator correspond to indices of the state. See map_wires() to convert operations to integer wire labels.

The shape of state should be [2]*num_wires.

This is a functools.singledispatch function, so additional specialized kernels for specific operations can be registered like:

@apply_operation.register
def _(op: type_op, state):
    # custom op application method here

Example:

>>> state = np.zeros((2,2))
>>> state[0][0] = 1
>>> state
tensor([[1., 0.],
    [0., 0.]], requires_grad=True)
>>> apply_operation(qml.X(0), state)
tensor([[0., 0.],
    [1., 0.]], requires_grad=True)

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