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- import numpy as np
- def naive_network_benchmarking(network, path_list, bounces, sample_times):
- '''Perform vanilla network benchmarking for each path in the `path_list`.
- Return a tuple: (a list of fidelities, the total number of bounces).
- '''
- fidelity = {}
- cost = 0
- for path in path_list:
- p, bounces_num = network.benchmark_path(path, bounces, sample_times)
- # fidelity.append(result)
- fidelity[path] = p + (1 - p) / 2 # Convert the estimated depolarizing parameter `p` into fidelity
- cost += bounces_num
- # print("Estimated fidelity:", fidelity)
- # best_path = np.argmax(fidelity) + 1
- best_path = max(fidelity, key=fidelity.get)
- correctness = best_path == network.best_path
- best_path_fidelity = fidelity[best_path]
- return correctness, cost, best_path_fidelity
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