cirq.ApplyMixtureArgs

class cirq.ApplyMixtureArgs(target_tensor: numpy.ndarray, out_buffer: numpy.ndarray, auxiliary_buffer0: numpy.ndarray, auxiliary_buffer1: numpy.ndarray, left_axes: Iterable[int], right_axes: Optional[Iterable[int]] = None)[source]

Arguments for performing a mixture of unitaries.

The receiving object is expected to mutate target_tensor so that it
contains the state (wavefunction or density matrix) after applying the
mixture then return target_tensor. Alternatively, if workspace is
required, the receiving object can overwrite out_buffer with the results
and return out_buffer. Or, if the receiving object is attempting to
be simple instead of fast, it can create an entirely new array and
return that.
target\_tensor
The input tensor that needs to be left (and potentially
right) multiplied and summed, representing the effect of the
mixture. The tensor will have the shape (2, 2, 2, …, 2). It can
correspond to a wavefunction or a density matrix.
out\_buffer
Pre-allocated workspace with the same shape and
dtype as the target tensor. If buffers are used, the result should
end up in this buffer. It is the responsibility of calling code
to notice if the result is this buffer.
auxiliary\_buffer0
Pre-allocated workspace with the same shape and dtype
as the target tensor.
auxiliary\_buffer1
Pre-allocated workspace with the same shape
and dtype as the target tensor.
left\_axes

Which axes to multiply the left action of the mixture upon.

right\_axes
Which axes to multiply the right action of the mixture upon.
If provided we will assume target_tensor is a density matrix,
otherwise it will be assuemd target_tensor is a wavefunction.
__init__(target_tensor: numpy.ndarray, out_buffer: numpy.ndarray, auxiliary_buffer0: numpy.ndarray, auxiliary_buffer1: numpy.ndarray, left_axes: Iterable[int], right_axes: Optional[Iterable[int]] = None)[source]

Args for apply mixture.

Parameters
  • target_tensor – The input tensor that needs to be left (and potentially right) multiplied and summed, representing the effect of the mixture. The tensor will have the shape (2, 2, 2, …, 2). It can correspond to a wavefunction or a density matrix.

  • out_buffer – Pre-allocated workspace with the same shape and dtype as the target tensor. If buffers are used, the result should end up in this buffer. It is the responsibility of calling code to notice if the result is this buffer.

  • auxiliary_buffer0 – Pre-allocated workspace with the same shape and dtype as the target tensor.

  • auxiliary_buffer1 – Pre-allocated workspace with the same shape and dtype as the target tensor.

  • left_axes – Which axes to multiply the left action of the mixture upon.

  • right_axes – Which axes to multiply the right action of the mixture upon. If provided we will assume target_tensor is a density matrix, otherwise it will be assuemd target_tensor is a wavefunction.