ああああああああああああああああああああああああああああああああああああああああああああああああああああああああああ

test 3

import numpy as np
import neal

def tsp_qubo(distances):
  n = distances.shape[0]
  Q = np.zeros((n, n))

  # Set diagonal elements to 0
  for i in range(n):
    Q[i][i] = 0

  # Set off-diagonal elements
  for i in range(n):
    for j in range(i+1, n):
      Q[i][j] = distances[i][j]
      Q[j][i] = distances[i][j]

  # Set quadratic bias
  q = np.ones(n) * -1

  return Q, q

# Example distances matrix
distances = np.array([[0, 10, 20, 30], [10, 0, 25, 35], [20, 25, 0, 15], [30, 35, 15, 0]])
Q, q = tsp_qubo(distances)

# Solve the problem using the D-Wave system
solver = neal.SimulatedAnnealingSampler()
response = solver.sample_qubo(Q, q)

# Print the solution
for sample in response.samples():
  print(sample)