sc-qmt/script_SDPexperiment.py at main · Algorithmiq/sc-qmt · GitHub

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from sc_qmt import *

theta = 2 * np.pi / 3
phi = 4 * np.pi / 3

v_0 = np.array([1, 0])
v_1 = np.array([1, np.sqrt(2)]) / np.sqrt(3)
v_2 = np.array([1, np.exp(theta * 1j) * np.sqrt(2)]) / np.sqrt(3)
v_3 = np.array([1, np.exp(phi * 1j) * np.sqrt(2)]) / np.sqrt(3)

M = []  # list of effects of the SIC-POVM
M.append(np.outer(v_0.conj().T, v_0) / 2)
M.append(np.outer(v_1.conj().T, v_1) / 2)
M.append(np.outer(v_2.conj().T, v_2) / 2)
M.append(np.outer(v_3.conj().T, v_3) / 2)

rhoPaulis = [
    np.array([[1, 0], [0, 0]]),
    np.array([[0, 0], [0, 1]]),
    np.array([[0.5 + 0.0j, 0.5 + 0.0j], [0.5 + 0.0j, 0.5 + 0.0j]]),
    np.array([[0.5 + 0.0j, -0.5 + 0.0j], [-0.5 - 0.0j, 0.5 + 0.0j]]),
    np.array([[0.5 + 0.0j, 0.0 + 0.5j], [0.0 - 0.5j, 0.5 + 0.0j]]),
    np.array([[0.5 + 0.0j, 0.0 - 0.5j], [0.0 + 0.5j, 0.5 + 0.0j]]),
]  # list of Pauli eigenstates

""" Here is an example of the code to simulate num_exps different single-delta SDP experiments for QMT of the SIC-POVM, with the Pauli eigenstates as input states, and with and without noise thereon
    """
num_exps = 1  # number of different experiments
num_shots = 100000  # number of shots per input state

vec_delta_ideal = np.zeros(
    num_exps
)  # we store here the SDP delta for each experiment (ideal case)
vec_delta_noisy001 = np.zeros(
    num_exps
)  # we store here the SDP delta for each experiment (incoherent noise)

for jj in range(num_exps):

    id_freq = generate_frequencies(num_shots, M, rhoPaulis)  # only shot noise
    noisy_freq = generate_frequencies_noisy(
        num_shots, M, rhoPaulis, 0.001, 0.0
    )  # incoherent noise with p = 0.001

    vec_delta_ideal[jj] = run_SDP_QMT(rhoPaulis, id_freq, SDPtype="singleDelta")[0]
    vec_delta_noisy001[jj] = run_SDP_QMT(rhoPaulis, noisy_freq, SDPtype="singleDelta")[
        0
    ]