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99 lines
4.0 KiB
99 lines
4.0 KiB
import json |
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import os.path |
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from alice import Alice |
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from bob import Bob |
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from channel import ChannelSym |
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from threading import Thread |
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import typing |
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import sympy as sp |
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from eve import EveBS |
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def run_qkd(alice: Alice, bob: Bob, n: int = 1000): |
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alice_thread = Thread(target=lambda: alice.generate_key(n)) |
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bob_thread = Thread(target=lambda: bob.generate_key(n)) |
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alice_thread.start() |
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bob_thread.start() |
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while alice_thread.is_alive() or bob_thread.is_alive(): |
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pass |
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def get_e_and_r(alice, bob, n=1000) -> typing.Tuple[float, float]: |
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return 1 - sum(k1 == k2 for k1, k2 in zip(alice.key, bob.key)) / len(alice.key), len(alice.key) / n |
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channel_parameters = {'p_opt': 0.05, 'p_dc': 0.05, 'mu': 1, 'detector_sensitivity': 0.8, 'transmittance': 0.8} |
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PAR_NAMES = {'p_opt': 'p_{opt}', 'p_dc': 'p_{dc}', 'mu': r'\mu', 'detector_sensitivity': r'\eta', 'transmittance': 't'} |
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mu, t, p_dc, p_opt, eta = sp.symbols(r'\mu t p_{dc} p_{opt} \eta') |
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e_theory = 0.5 * (p_dc + p_opt * mu * t * eta) / (2 * p_dc + mu * t * eta) |
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q_theory = (mu * t * eta + 2 * p_dc) / 2 * (1 - e_theory * 2) |
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def plot(parameter, values, add_eve: bool = False): |
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data_r, data_e = [], [] |
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data_r_theoretical, data_e_theoretical = [], [] |
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parameters_sp = {PAR_NAMES[pname]: channel_parameters[pname] for pname in PAR_NAMES.keys()} |
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n = 10000 |
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for val in values: |
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print(val) |
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channel = ChannelSym(**{parameter: val}) |
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alice, bob = Alice(channel), Bob(channel) |
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if add_eve: |
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eve = EveBS() |
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channel.eve = eve |
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run_qkd(alice, bob, n) |
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e, r = get_e_and_r(alice, bob, n) |
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data_r.append(r) |
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data_e.append(e) |
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parameters_sp[PAR_NAMES.get(parameter, parameter)] = val |
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data_r_theoretical.append(float(q_theory.evalf(subs=parameters_sp))) |
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data_e_theoretical.append(float(e_theory.evalf(subs=parameters_sp))) |
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from matplotlib import pyplot as plt |
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plt.plot(values, data_r, label='$R$') |
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plt.plot(values, data_e, label='$E$') |
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plt.plot(values, data_r_theoretical, label='$R_{theory}$') |
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plt.plot(values, data_e_theoretical, label='$E_{theory}$') |
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plt.legend() |
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if not os.path.exists('output'): |
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os.mkdir('output') |
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with open(f'output/dep_{parameter}.json', 'w') as f: |
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f.write(json.dumps([list(values), data_r, data_e, data_r_theoretical, data_e_theoretical])) |
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plt.xlabel('$' + PAR_NAMES.get(parameter, parameter) + '$') |
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plt.title('$R$ and $E$ vs. $' + PAR_NAMES.get(parameter, parameter) + '$' + ' with Eve' * add_eve) |
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plt.savefig(f'output/dep_{parameter}.png') |
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plt.show() |
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def run_one(): |
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N = 1000 |
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channel = ChannelSym() |
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alice, bob = Alice(channel), Bob(channel) |
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run_qkd(alice, bob) |
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# print(list(map(int, alice.key))) |
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# print(list(map(int, bob.key))) |
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print('Alice bits: ', *list(map(int, alice.sent))) |
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print('Bob bits: ', |
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*list(map(lambda t: {(0, 1): 1, (1, 0): 0, (0, 0): 2, (1, 1): 3}[(int(t[0]), int(t[1]))], bob.my_results))) |
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print('Alice basises:', *list(map(int, alice.my_basises))) |
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print('Bob basises: ', *list(map(int, bob.my_basises))) |
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bob_correctness = [left + right == 1 for left, right in bob.my_results] |
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print(' ', *[ |
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int(k) if c and b1 == b2 else ' ' |
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for c, k, b1, b2 in zip(bob_correctness, alice.sent, bob.my_basises, alice.my_basises)]) |
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print(' ', *[ |
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{(0, 1): 1, (1, 0): 0, (0, 0): 2, (1, 1): 3}[(int(k[0]), int(k[1]))] if c and b1 == b2 else ' ' |
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for c, k, b1, b2 in zip(bob_correctness, bob.my_results, bob.my_basises, alice.my_basises)]) |
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print( |
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f'{100 * sum(k1 == k2 for k1, k2 in zip(alice.key, bob.key)) / len(alice.key):.2f}%, ' |
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f'key length: {len(alice.key)}') |
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e, r = get_e_and_r(alice, bob, N) |
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print(f'E: {e * 100:.1f}%, R: {r}') |
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if __name__ == '__main__': |
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import numpy as np |
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plot('p_dc', np.arange(0, 0.1, 0.01)) |
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plot('detector_sensitivity', np.arange(0.5, 1, 0.05)) |
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plot('mu', np.arange(0.5, 1.5, 0.1))
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