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b/model/eve.py index 21bc49b..88c81cb 100644 --- a/model/eve.py +++ b/model/eve.py @@ -28,3 +28,32 @@ class EveBS: else: self.obtained_data.append(None) return passed + + +@dataclass +class EvePNS: + k: int = 1 + my_basises: typing.List[bool] = field(default_factory=list) + obtained_data: typing.List[typing.Optional[bool]] = field(default_factory=list) + + def process_photons(self, photons: typing.List[typing.Tuple[bool, bool]]) -> typing.List[typing.Tuple[bool, bool]]: + basis = choice([False, True]) + self.my_basises.append(basis) + clicks = [0, 0] + passed = [] + captured = 0 + for photon in photons: + if captured < self.k: + captured += 1 + photon_bit, photon_basis = photon + if photon_basis == basis: + clicks[photon_bit] += 1 + else: + clicks[choice([0, 1])] += 1 + else: + passed.append(photon) + if any(clicks) and not all(clicks): + self.obtained_data.append(bool(clicks[1])) + else: + self.obtained_data.append(None) + return passed diff --git a/model/main.py b/model/main.py index b2861d5..1999e0f 100644 --- a/model/main.py +++ b/model/main.py @@ -1,8 +1,12 @@ +import json +import os.path + from alice import Alice from bob import Bob from channel import ChannelSym from threading import Thread import typing +import sympy as sp from eve import EveBS @@ -21,11 +25,17 @@ def get_e_and_r(alice, bob, n=1000) -> typing.Tuple[float, float]: channel_parameters = {'p_opt': 0.05, 'p_dc': 0.05, 'mu': 1, 'detector_sensitivity': 0.8, 'transmittance': 0.8} +PAR_NAMES = {'p_opt': 'p_{opt}', 'p_dc': 'p_{dc}', 'mu': r'\mu', 'detector_sensitivity': r'\eta', 'transmittance': 't'} +mu, t, p_dc, p_opt, eta = sp.symbols(r'\mu t p_{dc} p_{opt} \eta') +e_theory = 0.5 * (p_dc + p_opt * mu * t * eta) / (2 * p_dc + mu * t * eta) +q_theory = (mu * t * eta + 2 * p_dc) / 2 * (1 - e_theory * 2) -def plot(parameter, values, add_eve: bool = True): +def plot(parameter, values, add_eve: bool = False): data_r, data_e = [], [] - n = 1000 + data_r_theoretical, data_e_theoretical = [], [] + parameters_sp = {PAR_NAMES[pname]: channel_parameters[pname] for pname in PAR_NAMES.keys()} + n = 10000 for val in values: print(val) channel = ChannelSym(**{parameter: val}) @@ -37,12 +47,22 @@ def plot(parameter, values, add_eve: bool = True): e, r = get_e_and_r(alice, bob, n) data_r.append(r) data_e.append(e) + parameters_sp[PAR_NAMES.get(parameter, parameter)] = val + data_r_theoretical.append(float(q_theory.evalf(subs=parameters_sp))) + data_e_theoretical.append(float(e_theory.evalf(subs=parameters_sp))) from matplotlib import pyplot as plt - plt.plot(values, data_r) - plt.plot(values, data_e) - plt.legend(['R', 'E']) - plt.xlabel({'p_dc': '$p_{dc}$'}.get(parameter, parameter)) - plt.title('$R$ and $E$ vs. ' + {'p_dc': '$p_{dc}$'}.get(parameter, parameter) + ' with Eve' * add_eve) + plt.plot(values, data_r, label='$R$') + plt.plot(values, data_e, label='$E$') + plt.plot(values, data_r_theoretical, label='$R_{theory}$') + plt.plot(values, data_e_theoretical, label='$E_{theory}$') + plt.legend() + if not os.path.exists('output'): + os.mkdir('output') + with open(f'output/dep_{parameter}.json', 'w') as f: + f.write(json.dumps([list(values), data_r, data_e, data_r_theoretical, data_e_theoretical])) + plt.xlabel('$' + PAR_NAMES.get(parameter, parameter) + '$') + plt.title('$R$ and $E$ vs. $' + PAR_NAMES.get(parameter, parameter) + '$' + ' with Eve' * add_eve) + plt.savefig(f'output/dep_{parameter}.png') plt.show() @@ -75,3 +95,5 @@ def run_one(): if __name__ == '__main__': import numpy as np plot('p_dc', np.arange(0, 0.1, 0.01)) + plot('detector_sensitivity', np.arange(0.5, 1, 0.05)) + plot('mu', np.arange(0.5, 1.5, 0.1))