Function for calculating the cross-entropy benchmarking fidelity

fidelity_xeb.py

def fidelity_xeb(n_qubits: int, trials: int, n_samples: int, sampler: Callable[[np.ndarray, int], float]) -> float:
  dim = 2**n_qubits
  # keep track of the ideal simulated probabilities
  ideal_probs = []
  # loop over the random programs
  for _ in range(trials):
      unitary = random_unitary(dim)
      sample_probs = [sampler(unitary, bb) for bb in range(dim)]
      samples = np.random.choice(dim, size=n_samples, p=sample_probs)
      for sample in samples:
          ideal_prob = simulate_probability(unitary, sample)
          ideal_probs.append(ideal_prob)

  return dim*np.mean(ideal_probs) - 1