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