33 lines
1.2 KiB
Python
33 lines
1.2 KiB
Python
import matplotlib.pyplot as plt
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import matplotlib
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import numpy as np
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import json
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matplotlib.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
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matplotlib.rc('text', usetex=True)
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matplotlib.rcParams.update({'errorbar.capsize': 2})
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results_naive = np.genfromtxt("qbit_scaling_naive.csv")
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results_graph = np.genfromtxt("qbit_scaling_graph.csv")
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with open("qbit_scaling_meta.json") as fin:
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meta = json.load(fin)
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h0 = plt.errorbar(results_naive[:, 0], results_naive[:, 2], results_naive[:, 3]
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, label=f"Dense Vector Simulator $N_c={int(results_naive[:, 1][0])}$ Circuits"
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, marker="o"
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, color="black")
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h1 = plt.errorbar(results_graph[:, 0], results_graph[:, 2], results_graph[:, 3]
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, label=f"Graphical Simulator $N_c={int(results_graph[:, 1][0])}$ Circuits"
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, marker="^"
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, color="black")
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plt.legend(handles=[h0, h1])
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plt.xlabel("Number of Qbits $N_q$")
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plt.ylabel("Execution Time per Circuit [s]")
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plt.title(f"Execution Time for ${meta['ngates_per_qbit']}\\times N_q$ Gates with Random Circuits (Rescaled)")
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plt.savefig("scaling_qbits_linear.png", dpi=400)
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plt.show()
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