cirq.SupportsApplyChannel

An object that can efficiently implement a channel.

cirq.SupportsApplyChannel(
    *args, **kwargs
)

Methods

_apply_channel_

View source

_apply_channel_(
    args: cirq.ApplyChannelArgs
) -> Union[np.ndarray, None, NotImplementedType]

Efficiently applies a channel.

This method is given both the target tensor and workspace of the same shape and dtype. The method then either performs inline modifications of the target tensor and returns it, or writes its output into the a workspace tensor and returns that. This signature makes it possible to write specialized simulation methods that run without performing large allocations, significantly increasing simulation performance.

Args
args A cirq.ApplyChannelArgs object with the args.target_tensor to operate on, an args.out_buffer, 'args.auxiliary_buffer0andargs.auxiliary_buffer1buffers to use as temporary workspace, and theargs.left_axesandargs.right_axesof the tensor to target with the unitary operation. Note that this method is permitted (and in fact expected) to mutateargs.target_tensor` and the given buffers.

Returns
If the receiving object is not able to apply a channel, None or NotImplemented should be returned.

If the receiving object is able to work inline, it should directly mutate args.target_tensor and then return args.target_tensor. The caller will understand this to mean that the result is in args.target_tensor.

If the receiving object is unable to work inline, it can write its output over args.out_buffer and then return args.out_buffer. The caller will understand this to mean that the result is in args.out_buffer (and so what was args.out will become args.target_tensor in the next call, and vice versa).

The receiving object is also permitted to allocate a new numpy.ndarray and return that as its result.