Quellcode fĂĽr qiskit.synthesis.linear.cnot_synth

# This code is part of Qiskit.
# (C) Copyright IBM 2017, 2019.
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
# Any modifications or derivative works of this code must retain this
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Implementation of the GraySynth algorithm for synthesizing CNOT-Phase
circuits with efficient CNOT cost, and the Patel-Hayes-Markov algorithm
for optimal synthesis of linear (CNOT-only) reversible circuits.

import copy
import numpy as np
from qiskit.circuit import QuantumCircuit
from qiskit.exceptions import QiskitError

[Doku]def synth_cnot_count_full_pmh(state, section_size=2): """ Synthesize linear reversible circuits for all-to-all architecture using Patel, Markov and Hayes method. This function is an implementation of the Patel, Markov and Hayes algorithm from [1] for optimal synthesis of linear reversible circuits for all-to-all architecture, as specified by an n x n matrix. Args: state (list[list] or ndarray): n x n boolean invertible matrix, describing the state of the input circuit section_size (int): the size of each section, used in the Patel–Markov–Hayes algorithm [1]. section_size must be a factor of num_qubits. Returns: QuantumCircuit: a CX-only circuit implementing the linear transformation. Raises: QiskitError: when variable "state" isn't of type numpy.ndarray References: 1. Patel, Ketan N., Igor L. Markov, and John P. Hayes, *Optimal synthesis of linear reversible circuits*, Quantum Information & Computation 8.3 (2008): 282-294. `arXiv:quant-ph/0302002 [quant-ph] <https://arxiv.org/abs/quant-ph/0302002>`_ """ if not isinstance(state, (list, np.ndarray)): raise QiskitError( "state should be of type list or numpy.ndarray, " "but was of the type {}".format(type(state)) ) state = np.array(state) # Synthesize lower triangular part [state, circuit_l] = _lwr_cnot_synth(state, section_size) state = np.transpose(state) # Synthesize upper triangular part [state, circuit_u] = _lwr_cnot_synth(state, section_size) circuit_l.reverse() for i in circuit_u: i.reverse() # Convert the list into a circuit of C-NOT gates circ = QuantumCircuit(state.shape[0]) for i in circuit_u + circuit_l: circ.cx(i[0], i[1]) return circ
def _lwr_cnot_synth(state, section_size): """ This function is a helper function of the algorithm for optimal synthesis of linear reversible circuits (the Patel–Markov–Hayes algorithm). It works like gaussian elimination, except that it works a lot faster, and requires fewer steps (and therefore fewer CNOTs). It takes the matrix "state" and splits it into sections of size section_size. Then it eliminates all non-zero sub-rows within each section, which are the same as a non-zero sub-row above. Once this has been done, it continues with normal gaussian elimination. The benefit is that with small section sizes (m), most of the sub-rows will be cleared in the first step, resulting in a factor m fewer row row operations during Gaussian elimination. The algorithm is described in detail in the following paper "Optimal synthesis of linear reversible circuits." Patel, Ketan N., Igor L. Markov, and John P. Hayes. Quantum Information & Computation 8.3 (2008): 282-294. Note: This implementation tweaks the Patel, Markov, and Hayes algorithm by adding a "back reduce" which adds rows below the pivot row with a high degree of overlap back to it. The intuition is to avoid a high-weight pivot row increasing the weight of lower rows. Args: state (ndarray): n x n matrix, describing a linear quantum circuit section_size (int): the section size the matrix columns are divided into Returns: numpy.matrix: n by n matrix, describing the state of the output circuit list: a k by 2 list of C-NOT operations that need to be applied """ circuit = [] num_qubits = state.shape[0] cutoff = 1 # Iterate over column sections for sec in range(1, int(np.floor(num_qubits / section_size) + 1)): # Remove duplicate sub-rows in section sec patt = {} for row in range((sec - 1) * section_size, num_qubits): sub_row_patt = copy.deepcopy(state[row, (sec - 1) * section_size : sec * section_size]) if np.sum(sub_row_patt) == 0: continue if str(sub_row_patt) not in patt: patt[str(sub_row_patt)] = row else: state[row, :] ^= state[patt[str(sub_row_patt)], :] circuit.append([patt[str(sub_row_patt)], row]) # Use gaussian elimination for remaining entries in column section for col in range((sec - 1) * section_size, sec * section_size): # Check if 1 on diagonal diag_one = 1 if state[col, col] == 0: diag_one = 0 # Remove ones in rows below column col for row in range(col + 1, num_qubits): if state[row, col] == 1: if diag_one == 0: state[col, :] ^= state[row, :] circuit.append([row, col]) diag_one = 1 state[row, :] ^= state[col, :] circuit.append([col, row]) # Back reduce the pivot row using the current row if sum(state[col, :] & state[row, :]) > cutoff: state[col, :] ^= state[row, :] circuit.append([row, col]) return [state, circuit]