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@ -24,46 +24,82 @@ 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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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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def common_alice_bob_eve(alice: Alice, eve, n=1000) -> float: |
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return sum(correctness and alice_bit == eve_bit for alice_bit, eve_bit, correctness in |
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zip(alice.key, eve.obtained_data, alice.correctness)) / 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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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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PAR_NAMES = {'p_opt': 'p_{opt}', 'p_dc': 'p_{dc}', 'mu': r'\mu', |
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'detector_sensitivity': r'\eta', 'transmittance': 't', |
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'eve:hijack_probability': r'p_{Eve}'} |
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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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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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p1 = 2 * p_dc - p_dc ** 2 |
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q_theory = (mu * t * eta + 2 * p_dc) / 2 * (1 - e_theory * 2) |
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p_ea, p_eb = sp.exp(-mu * (1 - eta) * p_opt), sp.exp(mu * (eta + p_opt - eta * p_opt - 1)) |
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e_theory = 0.5 * (p_dc + t * (1 - p_ea) - p_dc * t * (1 - p_ea)) \ |
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* (1 - p_dc / 2 - 0.5 * t * (1 - p_eb) + 0.5 * p_dc * t * (1 - p_eb)) |
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q_theory = 0.5 * (p1 + (1 - p1) * (1 - sp.exp(-mu * eta)) * t) |
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e_theory /= q_theory |
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def plot(parameter, values, add_eve: bool = False): |
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def plot(parameter, values, add_eve: bool = False, show=False): |
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data_r, data_e = [], [] |
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data_q, data_e, eve_gains = [], [], [] |
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data_r_theoretical, data_e_theoretical = [], [] |
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data_q_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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parameters_sp = {PAR_NAMES[pname]: channel_parameters[pname] for pname in channel_parameters.keys()} |
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n = 10000 |
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n = 10000 |
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for val in values: |
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for val in values: |
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print(val) |
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print(val) |
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channel = ChannelSym(**{parameter: val}) |
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channel = ChannelSym(**({parameter: val} if not parameter.startswith('eve:') else {}), |
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**{par: channel_parameters[par] for par in channel_parameters.keys() if par != parameter}) |
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alice, bob = Alice(channel), Bob(channel) |
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alice, bob = Alice(channel), Bob(channel) |
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if add_eve: |
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if add_eve: |
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eve = EveBS() |
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eve = EveBS(**({parameter.removeprefix('eve:'): val} if parameter.startswith('eve:') else {})) |
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channel.eve = eve |
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channel.eve = eve |
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run_qkd(alice, bob, n) |
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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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e, r = get_e_and_r(alice, bob, n) |
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data_r.append(r) |
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data_q.append(r) |
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data_e.append(e) |
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data_e.append(e) |
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if add_eve: |
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eve_gains.append(common_alice_bob_eve(alice, eve, n)) |
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parameters_sp[PAR_NAMES.get(parameter, parameter)] = val |
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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_q_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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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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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_q, label='$Q$') |
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plt.plot(values, data_e, label='$E$') |
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plt.plot(values, data_q_theoretical, label='$Q_{theory}$') |
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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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plt.legend() |
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if not os.path.exists('output'): |
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if not os.path.exists('output'): |
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os.mkdir('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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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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f.write(json.dumps([list(values), data_q, data_e, data_q_theoretical, data_e_theoretical])) |
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plt.xlabel('$' + PAR_NAMES.get(parameter, parameter) + '$') |
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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.title('$Q$ 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.savefig(f'output/dep_{parameter}_q{"_with_eve" * add_eve}.png') |
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if show: |
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plt.show() |
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plt.show() |
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else: |
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plt.cla() |
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plt.plot(values, data_e, label='$E$') |
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plt.plot(values, data_e_theoretical, label='$E_{theory}$') |
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plt.legend() |
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plt.xlabel('$' + PAR_NAMES.get(parameter, parameter) + '$') |
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plt.title('$E$ vs. $' + PAR_NAMES.get(parameter, parameter) + '$' + ' with Eve' * add_eve) |
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plt.savefig(f'output/dep_{parameter}_e{"_with_eve" * add_eve}.png') |
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if show: |
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plt.show() |
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else: |
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plt.cla() |
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if add_eve: |
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plt.plot(values, [g / q for g, q in zip(eve_gains, data_q)], label='$Q_{Eve}$') |
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plt.title('Eve\'s gains') |
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plt.legend() |
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plt.ylabel(r'$\dfrac{Q_{Eve}}{Q}$') |
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plt.xlabel('$' + PAR_NAMES.get(parameter, parameter) + '$') |
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plt.savefig(f'output/dep_{parameter}_eve_q.png') |
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if show: |
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plt.show() |
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else: |
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plt.cla() |
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def run_one(): |
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def run_one(): |
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@ -94,6 +130,10 @@ def run_one(): |
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if __name__ == '__main__': |
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if __name__ == '__main__': |
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import numpy as np |
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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('p_dc', np.arange(0, 0.15, 0.01)) |
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plot('mu', np.arange(0.5, 1.5, 0.1)) |
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plot('p_opt', np.arange(0, 0.15, 0.01)) |
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plot('detector_sensitivity', np.arange(0.1, 1, 0.05)) |
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plot('mu', np.arange(0.5, 2.5, 0.1)) |
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plot('transmittance', np.arange(0.4, 1, 0.05)) |
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# plot('eve:hijack_probability', np.arange(0, 1, 0.05)) |
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