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analyse_estimation.py
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analyse_estimation.py
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import qsharp
from PhaseEstimation import run
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
phi = 0
nb_run = 20
def estim_phi(n_shots, phi, n_oracle):
phi_esti = 0
for _ in range(nb_run):
result = run.simulate(nShots=n_shots, phi=phi, oraclePower=n_oracle)
p_esti = result[1] / n_shots
phi_esti += (2 / n_oracle) * (np.arcsin(np.sqrt(p_esti)) - np.pi / 4)
return phi_esti / nb_run
nb_range = 200
delta_phi_nshots = []
print("Processing n_shots")
for n in range(1, nb_range):
delta_phi_nshots.append(abs(phi - estim_phi(n, phi, 10)))
print("Processing n_oracle")
delta_phi_noracles = []
for n in range(10, nb_range):
delta_phi_noracles.append(abs(phi - estim_phi(10, phi, n)))
plt.plot(range(10, nb_range), delta_phi_nshots, label="nshots")
plt.plot(range(10, nb_range), delta_phi_noracles, label="n_oracles")
plt.title("Comparaison de la precision en fonction de n_shots et n_oracles")
plt.xlabel("n_shots ou n_oracles")
plt.ylabel("precision")
plt.legend()
X, reg_oracles, reg_shots = [], [], []
for i in range(len(delta_phi_noracles)):
reg_oracles.append(np.log(delta_phi_noracles[i]))
reg_shots.append(np.log(delta_phi_nshots[i]))
X.append(np.log(i))
plt.figure()
plt.plot(X, reg_shots, label="nshots")
plt.plot(X, reg_oracles, label="noracles")
plt.title("Regression lineaire pour n_shots et n_oracles")
plt.xlabel("log(n)")
plt.ylabel("-a log(n) + log(b)")
plt.legend()
deg_oracles, _, _, _, _ = stats.linregress(X, reg_oracles)
deg_shots, _, _, _, _ = stats.linregress(X, reg_shots)
print(
f"Alpha pour la variation d'oracles: {deg_oracles}\nAlpha pour la variation de shots:{deg_shots}"
)