pymatching

import pymatching import numpy as np import scipy.sparse # Example: Parity check matrix (H) for a simple [3,1,2] repetition code # This H matrix represents two stabilizers: qubit0^qubit1 and qubit1^qubit2 H = scipy.sparse.csc_matrix(np.array([ [1, 1, 0], [0, 1, 1] ])) # Initialize the Matching object from the parity check matrix matching = pymatching.Matching(H) # Simulate a syndrome measurement (e.g., after an error on qubit 1) # An error on qubit 1 flips both stabilizers (syndrome = [1, 1]) syndrome = np.array([1, 1]) # Decode the syndrome to find the most likely error pattern # The result 'correction' is a binary vector indicating the predicted error locations correction = matching.decode(syndrome) print(f"Input syndrome: {syndrome}") print(f"Predicted error pattern: {correction}") # Expected output for syndrome [1,1]: [0, 1, 0] (error on qubit 1)