bachelor_thesis/presentation/spin_chain/time_evolution.py

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import numpy as np
import matplotlib.pyplot as plt
from pyqcs import State, sample
from transfer_matrix import T_time_slice
from hamiltonian import H
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from scipy.linalg import expm
nqbits = 4
g = 0.5
N = 400
t_stop = 9
delta_t = 0.05
qbits = list(range(nqbits))
n_sample = 200
measure = 0b10
results_qc = []
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results_np = []
print()
for t in np.arange(0, t_stop, delta_t):
# QC simulation
state = State.new_zero_state(nqbits)
for _ in range(N):
state = T_time_slice(qbits, t, g, N) * state
result = sample(state, measure, n_sample)
results_qc.append(result[0] / n_sample)
# Simulation using matrices
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np_zero_state = np.zeros(2**nqbits)
np_zero_state[0] = 1
T = expm(-1j * t * H(nqbits, g))
#for Tv in T:
# print(np.sum(np.abs(Tv)))
# assert np.isclose(np.sum(np.abs(Tv)), 1)
np_state = T.dot(np_zero_state)
amplitude = np.sum(np.abs(np_state[[False if (i & measure) else True for i in range(2**nqbits)]]))
results_np.append(amplitude)
print(f"simulating... {int(t/t_stop*100)} % ", end="\r")
print()
print("done.")
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h0, = plt.plot(np.arange(0, t_stop, delta_t), results_qc, label=f"Quantum computing {n_sample} samples")
h1, = plt.plot(np.arange(0, t_stop, delta_t), results_np, label="Classical simulation using explicit transfer matrix")
plt.xlabel("t")
plt.ylabel(r"$|0\rangle$ probability amplitude for second spin")
plt.title(f"{nqbits} site spin chain with g={g} coupling to external field")
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plt.legend(handles=[h0, h1])
plt.show()