channel_props.choi_rank ======================= .. py:module:: channel_props.choi_rank .. autoapi-nested-parse:: Calculates the Choi rank of a channel. Functions --------- .. autoapisummary:: channel_props.choi_rank.choi_rank Module Contents --------------- .. py:function:: choi_rank(phi) Calculate the rank of the Choi representation of a quantum channel. (Section 2.2: Quantum Channels from :cite:`Watrous_2018_TQI`). .. rubric:: Examples The transpose map can be written either in Choi representation (as a SWAP operator) or in Kraus representation. If we choose the latter, it will be given by the following matrices: .. math:: \begin{equation} \frac{1}{\sqrt{2}} \begin{pmatrix} 0 & i \\ -i & 0 \end{pmatrix}, \quad \frac{1}{\sqrt{2}} \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix}, \quad \begin{pmatrix} 1 & 0 \\ 0 & 0 \end{pmatrix}, \quad \begin{pmatrix} 0 & 0 \\ 0 & 1 \end{pmatrix}. \end{equation} and can be generated in :code:`|toqito⟩` with the following list: .. jupyter-execute:: import numpy as np from toqito.channel_props import choi_rank kraus_1 = np.array([[1, 0], [0, 0]]) kraus_2 = np.array([[1, 0], [0, 0]]).conj().T kraus_3 = np.array([[0, 1], [0, 0]]) kraus_4 = np.array([[0, 1], [0, 0]]).conj().T kraus_5 = np.array([[0, 0], [1, 0]]) kraus_6 = np.array([[0, 0], [1, 0]]).conj().T kraus_7 = np.array([[0, 0], [0, 1]]) kraus_8 = np.array([[0, 0], [0, 1]]).conj().T kraus_ops = [[kraus_1, kraus_2], [kraus_3, kraus_4],[kraus_5, kraus_6],[kraus_7, kraus_8]] choi_rank(kraus_ops) We can the verify the associated Choi representation (the SWAP gate) gets the same Choi rank: .. jupyter-execute:: choi_matrix = np.array([[1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]]) choi_rank(choi_matrix) .. rubric:: References .. bibliography:: :filter: docname in docnames :raises ValueError: If matrix is not Choi. :param phi: Either a Choi matrix or a list of Kraus operators :return: The Choi rank of the provided channel representation.