69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
from collections import deque
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import matplotlib.pyplot as plt
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import numpy as np
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from pyqcs import State, H, X, S, CZ
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from pyqcs.graph.state import GraphState
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from pyqcs.util.random_circuits import random_circuit
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from measure_circuit import execution_statistics
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def S_with_extra_arg(act, i):
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return S(act)
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def test_scaling_qbits(state_factory
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, nstart
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, nstop
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, ngates_per_qbit
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, ncircuits
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, **kwargs):
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results = deque()
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for qbits in range(nstart, nstop):
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circuits = [random_circuit(qbits, ngates_per_qbit * qbits, X, H, S_with_extra_arg, CZ)
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for _ in range(ncircuits)]
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state = state_factory(qbits)
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print("running test with", qbits, "qbits")
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N, avg, std_dev = execution_statistics(circuits, state, scale=qbits, **kwargs)
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results.append([qbits, N, avg, std_dev])
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return np.array(results, dtype=np.double)
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if __name__ == "__main__":
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nstart = 4
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nstop = 19
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ncircuits = 250
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ngates_per_qbit = 100
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np.random.seed(0xdeadbeef)
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results_naive = test_scaling_qbits(State.new_zero_state
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, nstart
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, nstop
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, ngates_per_qbit
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, ncircuits
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, repeat=10)
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np.random.seed(0xdeadbeef)
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results_graph = test_scaling_qbits(GraphState.new_zero_state
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, nstart
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, nstop
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, ngates_per_qbit
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, ncircuits
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, repeat=10)
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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 ${ngates_per_qbit}\\times N_q$ Gates with random Circuits (rescaled)")
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#plt.show()
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plt.savefig("Figure_1.png", dpi=400)
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