add useful scripts

scripts/plot.py

+import matplotlib.pyplot as plt
+import numpy as np
+
+def calc_average_time_to_fail(p, measure_rounds):
+    return - measure_rounds / np.log2(p)
+
+
+def attf_plot(times, p_locals, measure_rounds = 30, name = None):
+    fig = plt.figure()
+    for scale, ps in times.items():
+        ps = np.array(ps)
+        ps = calc_average_time_to_fail(ps, measure_rounds)
+        plt.plot(p_locals[0:], ps[0:], "-+")
+        # plt.legend(str(scale))
+    plt.legend(list(map(str, times.keys())))
+    plt.xlabel(r"error rate $p/\%$")
+    plt.ylabel(r"average rounds to failure")
+    # plt.show()
+    if name is not None:
+        fig.savefig(name)
+
+def plot2():
+    fig = plt.figure()
+    p3 = np.arange(0.4, 0.85, 0.05)
+    data_2 = {
+        #p   0.004   0.005   0.006   0.007   0.008   0.009   1.000
+        8:  [0.9362, 0.9060, 0.8605, 0.8180, 0.7735, 0.7272, 0.6883],
+        12: [0.9553, 0.9212, 0.8674, 0.8166, 0.7481, 0.7000, 0.6519],
+        16: [0.9612, 0.9296, 0.8778, 0.8144, 0.7472, 0.6760, 0.6046],
+        20: [0.9785, 0.9460, 0.8940, 0.7965, 0.7395, 0.6340, 0.5995],
+    }
+
+# original
+data_1 = {
+    #p1 0.002    0.003   0.004   0.0045  0.005   0.0055
+    8:  [0.9755, 0.9258, 0.8533, 0.811,  0.7644, 0.7057],
+    12: [0.9900, 0.9558, 0.8811, 0.83,   0.7559, 0.6936],
+    16: [0.9948, 0.977,  0.8966, 0.8392, 0.7602, 0.6742],
+    20: [0.9975, 0.978,  0.907,  0.828,  0.757,  0.6445]
+}
+
+# 以下 p 均为光子与qubit相互作用(e^{iπ \hat{n}}, decode)的error rate
+# 直接利用光子测量 
+data_2 = {
+    #p   0.004   0.005   0.006   0.007   0.008   0.009   1.000
+    8:  [0.9362, 0.9060, 0.8605, 0.8180, 0.7735, 0.7272, 0.6883],
+    12: [0.9553, 0.9212, 0.8674, 0.8166, 0.7481, 0.7000, 0.6519],
+    16: [0.9612, 0.9296, 0.8778, 0.8144, 0.7472, 0.6760, 0.6046],
+    20: [0.9785, 0.9460, 0.8940, 0.7965, 0.7395, 0.6340, 0.5995],
+}
+data_2 = {
+    #p   0.000   0.0005  0.001   0.0015  0.002  0.0025   0.003   0.0035
+    8:  [0.9996, 0.9846, 0.9606, 0.9156, 0.8678, 0.7932, 0.7104, 0.6534],
+    12: [1,      0.9938, 0.9792, 0.9434, 0.868,  0.7776, 0.6948, 0.6152],
+    16: [1,      0.997,  0.982,  0.958,  0.878,  0.763,  0.632,  0.562], 
+    # 20: [0.9785, 0.9460, 0.8940, 0.7965, 0.7395, 0.6340, 0.5995],
+}
+
+# 利用bosonic code建立纠缠, 取p_local = 0, 0.003, 0.006三种情况
+data_0000 = {
+    8: [0.9986, 0.9992, 0.9986, 0.9987, 0.9983, 0.9991, 0.9988, 0.9990, 0.9989],
+    12: [0.9996, 0.9997, 0.9998, 0.9994, 1, 0.9999, 0.9999, 0.9998, 0.9998],
+    16: [1, 1, 1, 1, 1, 1, 1, 0.9998, 1],
+    20: [1, 1, 1, 1, 1, 1, 1, 1, 1]
+}
+
+data_0003 = {
+    #p   0.004   0.0045  0.005   0.0055  0.006   0.0065  0.007   0.0075  0.008
+    8:  [0.9574, 0.9594, 0.9569, 0.9562, 0.9578, 0.9603, 0.9552, 0.9553, 0.9564],
+    12: [0.9743, 0.9743, 0.9743, 0.9754, 0.9755, 0.9754, 0.9753, 0.9701, 0.9708],
+    16: [0.9832, 0.9794, 0.9852, 0.9808, 0.9820, 0.9820, 0.9838, 0.9832, 0.9816],
+    20: [0.9887, 0.9910, 0.9900, 0.9880, 0.9887, 0.9923, 0.9873, 0.9887, 0.9917]
+}
+
+data_0003 = {
+    #p   0.000   0.001   0.002
+    8:  [0.962,  0.9618, 0.9598],
+    12: [0.9778, 0.9762, 0.9714],
+    16: [0.983,  0.978,  0.985], 
+}
+data_00045 = {
+    #p   0.000   0.001   0.002
+    8:  [0.887,  0.914,  0.8954],
+    12: [0.9064, 0.9074, 0.8988],
+    16: [0.913,  0.8902, 0.905], 
+}
+
+data_0006 = {
+    #p   0.004   0.0045  0.005   0.0055  0.006   0.0065  0.007   0.0075  0.008
+    8:  [0.7956, 0.7975, 0.7927, 0.7962, 0.8003, 0.7967, 0.7950, 0.7885, 0.7957],
+    12: [0.7854, 0.7758, 0.7788, 0.7859, 0.7881, 0.7694, 0.7767, 0.7694, 0.7712],
+    16: [0.7624, 0.7734, 0.7636, 0.7728, 0.7782, 0.7550, 0.7592, 0.7648, 0.7614],
+    20: [0.7617, 0.7567, 0.7613, 0.7633, 0.7543, 0.7613, 0.7633, 0.7436, 0.7450]
+}
+
+
+
+p1 = [0.002, 0.003, 0.004, 0.0045, 0.005, 0.0055]
+p2 = np.arange(0.4, 1.1, 0.1)
+p2 = np.arange(0.0, 0.4, 0.05)
+p3 = np.arange(0.4, 0.85, 0.05)
+p3 = [0., 0.1, 0.2]
+# p3 = np.arange(0.4, 0.75, 0.05)
+
+if __name__ == '__main__':
+
+    print(p3)
+    # attf_plot(data_1, p1, 30)
+    # attf_plot(data_2, p2, 30, "photon_measurement.png")
+    # attf_plot(data_2, p2, 30, "photon_measurement2.png")
+    attf_plot(data_0003, p3, 30, "bosonic_code_puri_0.003_2.png")
+    attf_plot(data_00045, p3, 30, "bosonic_code_puri_0.0045_2.png")
+    # attf_plot(data_0006, p3, 30, "bosonic_code_puri_0.006.png")

scripts/write_channel.py

+import sys
+import numpy as np
+
+# pm = 0.989939
+# p1 = 0.0
+# p2 = 0.0
+
+def pn(p, n):
+    if n == 0:
+        return 1 - 3 * p
+    else:
+        return p
+
+def gen_coeff(p):
+    coeff = []
+    for i in range(4):
+        for j in range(4):
+            for k in range(4):
+                for l in range(4):
+                    coeff.append(pn(p, i) * pn(p, j) * pn(p, k) * pn(p, l))
+
+    return np.array(coeff)
+
+if __name__ == '__main__':
+    np.set_printoptions(suppress=True)
+    argv = sys.argv
+    pm = float(argv[1])
+    p1 = float(argv[2])
+    p2 = float(argv[3])
+
+    with open("{:4f}-{:4f}-{:4f}-channel.txt".format(pm, p1, p2), 'w') as f:
+        correct = (1 - pm) * gen_coeff(p1)
+        # print(correct)
+        for coeff in correct:
+            f.write(str(coeff))
+            f.write(' ')
+        f.write('\n')
+    
+        error = pm * gen_coeff(p2)
+        # print(error)
+        for coeff in error:
+            f.write(str(coeff))
+            f.write(' ')