qml.devices.qubit.apply_operation¶
-
apply_operation
(op, state, is_state_batched=False, debugger=None, **_)[source]¶ -
apply_operation
(op, state, is_state_batched=False, debugger=None, **execution_kwargs) -
apply_operation
(op, state, is_state_batched=False, debugger=None, **execution_kwargs) -
apply_operation
(op, state, is_state_batched=False, debugger=None, **_) -
apply_operation
(op, state, is_state_batched=False, debugger=None, **_) -
apply_operation
(op, state, is_state_batched=False, debugger=None, **_) -
apply_operation
(op, state, is_state_batched=False, debugger=None, **_) -
apply_operation
(op, state, is_state_batched=False, debugger=None, **_) -
apply_operation
(op, state, is_state_batched=False, debugger=None, **_) -
apply_operation
(op, state, is_state_batched=False, debugger=None, **_) -
apply_operation
(op, state, is_state_batched=False, debugger=None, **_) -
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
- 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)