significantly improved time measurements
This commit is contained in:
parent
0cd2dfa8b4
commit
debcc57738
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@ -2,12 +2,13 @@ from collections import deque
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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import numpy as np
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import numpy as np
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import json
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import json
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from random import shuffle
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from pyqcs import State, H, X, S, CZ, M, list_to_circuit
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from pyqcs import State, H, X, S, CZ, M, list_to_circuit
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from pyqcs.graph.state import GraphState
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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 pyqcs.util.random_circuits import random_circuit
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from measure_circuit import execution_statistics
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from measure_circuit import execution_statistics, measure_all
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def S_with_extra_arg(act, i):
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def S_with_extra_arg(act, i):
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return S(act)
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return S(act)
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@ -18,21 +19,35 @@ def test_scaling_qbits(state_factory
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, ngates_per_qbit
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, ngates_per_qbit
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, ncircuits
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, ncircuits
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, **kwargs):
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, **kwargs):
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results = deque()
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trials = deque()
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for qbits in range(nstart, nstop):
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N = (nstop - nstart)
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print()
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for n, qbits in enumerate(range(nstart, nstop)):
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print(f"generating test data... {int(n/N * 100)} %", end="\r", flush=True)
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measurement_circuit = list_to_circuit([M(i) for i in range(qbits)])
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measurement_circuit = list_to_circuit([M(i) for i in range(qbits)])
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circuits = [random_circuit(qbits, ngates_per_qbit * qbits, X, H, S_with_extra_arg, CZ)
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circuits = [random_circuit(qbits, ngates_per_qbit * qbits, X, H, S_with_extra_arg, CZ)
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| measurement_circuit
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| measurement_circuit
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for _ in range(ncircuits)]
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for _ in range(ncircuits)]
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state = state_factory(qbits)
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state = state_factory(qbits)
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for circuit in circuits:
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trials.append((qbits, circuit, state))
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print("generating test data... done ")
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print("running test with", qbits, "qbits")
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print("randomizing tests...", end="", flush=True)
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N, avg, std_dev = execution_statistics(circuits, state, scale=qbits, **kwargs)
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shuffle(trials)
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print(" done")
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results = measure_all(trials, **kwargs)
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N, avg, std_dev = execution_statistics(results, scale={i:i for i in range(nstart, nstop)}, **kwargs)
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nqbits = [[i] for i in sorted(results.keys())]
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N = [[i] for i in N]
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avg = [[i] for i in avg]
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std_dev = [[i] for i in std_dev]
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return np.hstack([nqbits, N, avg, std_dev])
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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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if __name__ == "__main__":
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nstart = 4
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nstart = 4
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@ -1,5 +1,5 @@
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import timeit
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import timeit
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from collections import deque
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from collections import deque, defaultdict
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import numpy as np
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import numpy as np
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def measure_execution_time(circuit, state, repeat=100, **kwargs):
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def measure_execution_time(circuit, state, repeat=100, **kwargs):
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@ -14,17 +14,35 @@ def measure_execution_time(circuit, state, repeat=100, **kwargs):
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results.append(stop - start)
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results.append(stop - start)
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return min(results)
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return min(results)
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def measure_all(input_circuits_and_states, **kwargs):
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results = defaultdict(deque)
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def execution_statistics(circuits, state, scale=1, **kwargs):
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N = len(input_circuits_and_states)
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results = deque()
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for n, (key, circuit, state) in enumerate(input_circuits_and_states):
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print(f"running tests... {int(n/N * 100)} %", end="\r", flush=True)
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results[key].append(measure_execution_time(circuit, state, **kwargs))
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print("running tests... done ")
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return results
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for circuit in circuits:
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results.append(measure_execution_time(circuit, state, **kwargs))
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results = np.array(results, dtype=np.double)
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def execution_statistics(results, scale=1, **kwargs):
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results /= scale
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Ns = deque()
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N = len(results)
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avgs = deque()
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avg = np.average(results)
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std_devs = deque()
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std_dev = np.std(results) / np.sqrt(N)
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for k,v in sorted(results.items(), key=lambda x: x[0]):
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result = np.array(v, dtype=np.double)
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if(scale is not None):
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result /= scale[k]
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N = len(result)
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avg = np.average(result)
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std_dev = np.std(result) / np.sqrt(N)
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return N, avg, std_dev
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Ns.append(N)
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avgs.append(avg)
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std_devs.append(std_dev)
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Ns = np.array(Ns).astype(np.int64)
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avgs = np.array(avgs).astype(np.double)
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std_devs = np.array(std_devs).astype(np.double)
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return Ns, avgs, std_devs
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@ -1,12 +1,12 @@
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4.000000000000000000e+00 5.000000000000000000e+01 3.428762500334414762e-04 1.776446001982484369e-07
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@ -1,12 +1,12 @@
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@ -1,48 +1,48 @@
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2.300000000000000000e+03 1.000000000000000000e+02 9.641536674978851140e-02 9.073162453149088199e-04
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2.550000000000000000e+03 1.000000000000000000e+02 1.125992783100082245e-01 9.260318460859097867e-04
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2.600000000000000000e+03 1.000000000000000000e+02 1.181163900302271974e-01 9.457865410662973440e-04
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2.700000000000000000e+03 1.000000000000000000e+02 1.193943367699466795e-01 8.656900612182483667e-04
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||||||
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|
|
||||||
|
|
|
@ -1,48 +1,48 @@
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||||||
4.000000000000000000e+02 1.000000000000000000e+02 1.855520929748308767e-03 1.713134491388978309e-06
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4.000000000000000000e+02 1.000000000000000000e+02 1.835213840167853076e-03 1.654947914003680341e-06
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4.500000000000000000e+02 1.000000000000000000e+02 2.054871710352017440e-03 2.190088685886722304e-06
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4.500000000000000000e+02 1.000000000000000000e+02 2.028433730010874726e-03 2.073783279640639355e-06
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5.000000000000000000e+02 1.000000000000000000e+02 2.246458490408258495e-03 2.160730405732677556e-06
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5.000000000000000000e+02 1.000000000000000000e+02 2.218099229830841276e-03 1.976833413920745400e-06
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5.500000000000000000e+02 1.000000000000000000e+02 2.458994149783393524e-03 2.632555491360094470e-06
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5.500000000000000000e+02 1.000000000000000000e+02 2.425619929963431735e-03 1.999231091115152094e-06
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6.000000000000000000e+02 1.000000000000000000e+02 2.651202810593531606e-03 2.129537731470889621e-06
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6.000000000000000000e+02 1.000000000000000000e+02 2.619029390261857701e-03 2.123180092364623173e-06
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6.500000000000000000e+02 1.000000000000000000e+02 2.848435070191044239e-03 2.101762964151014804e-06
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6.500000000000000000e+02 1.000000000000000000e+02 2.810280509947915561e-03 2.604110830106028074e-06
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7.000000000000000000e+02 1.000000000000000000e+02 3.045234929813886871e-03 2.196795878570572359e-06
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7.000000000000000000e+02 1.000000000000000000e+02 3.004202880292723352e-03 2.290159355182151633e-06
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7.500000000000000000e+02 1.000000000000000000e+02 3.264530430024024064e-03 3.813739568843195435e-06
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7.500000000000000000e+02 1.000000000000000000e+02 3.204109490288828757e-03 2.401836261014467913e-06
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8.000000000000000000e+02 1.000000000000000000e+02 3.448390289340750106e-03 2.506359178657043656e-06
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8.000000000000000000e+02 1.000000000000000000e+02 3.402122789921122482e-03 2.613577702092499025e-06
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8.500000000000000000e+02 1.000000000000000000e+02 3.642879379913211042e-03 2.717216465339945572e-06
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8.500000000000000000e+02 1.000000000000000000e+02 3.599267799836525315e-03 2.760873850734378321e-06
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9.000000000000000000e+02 1.000000000000000000e+02 3.840055859691346961e-03 3.372217748148944484e-06
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9.000000000000000000e+02 1.000000000000000000e+02 3.792393579897179560e-03 3.552906026928584379e-06
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9.500000000000000000e+02 1.000000000000000000e+02 4.042165709906839384e-03 3.061728505191626180e-06
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9.500000000000000000e+02 1.000000000000000000e+02 3.983666429849108855e-03 3.432157696986986298e-06
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1.000000000000000000e+03 1.000000000000000000e+02 4.258553239924367691e-03 3.180552461228480271e-06
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1.000000000000000000e+03 1.000000000000000000e+02 4.189837399717361716e-03 3.086862551918502448e-06
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1.050000000000000000e+03 1.000000000000000000e+02 4.435837380369776241e-03 3.141057651670607941e-06
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1.050000000000000000e+03 1.000000000000000000e+02 4.370275900182605394e-03 3.099666612100548059e-06
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1.100000000000000000e+03 1.000000000000000000e+02 4.647524749525473037e-03 3.577954400671338468e-06
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1.100000000000000000e+03 1.000000000000000000e+02 4.568666459781525226e-03 3.082617759197979633e-06
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1.150000000000000000e+03 1.000000000000000000e+02 4.838921920163556829e-03 3.755994502604949956e-06
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1.150000000000000000e+03 1.000000000000000000e+02 4.768872990207455483e-03 2.842545899522316566e-06
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1.200000000000000000e+03 1.000000000000000000e+02 5.015973980407579733e-03 3.416063646669190170e-06
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1.200000000000000000e+03 1.000000000000000000e+02 4.967560990044148431e-03 3.357707473554368195e-06
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1.250000000000000000e+03 1.000000000000000000e+02 5.232391719837323728e-03 3.990654739077853341e-06
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1.250000000000000000e+03 1.000000000000000000e+02 5.167138569922826753e-03 3.403662596095571541e-06
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1.300000000000000000e+03 1.000000000000000000e+02 5.426057579607004508e-03 3.905549309310580046e-06
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1.300000000000000000e+03 1.000000000000000000e+02 5.357951160076482137e-03 3.668284912720865696e-06
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1.350000000000000000e+03 1.000000000000000000e+02 5.629395610012580091e-03 4.224554521711868355e-06
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1.350000000000000000e+03 1.000000000000000000e+02 5.556991380035469948e-03 3.333100383138736077e-06
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||||||
1.400000000000000000e+03 1.000000000000000000e+02 5.827894089889013897e-03 4.100545851548671890e-06
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1.400000000000000000e+03 1.000000000000000000e+02 5.744708620004530635e-03 4.047155267053082305e-06
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||||||
1.450000000000000000e+03 1.000000000000000000e+02 6.030842360341921032e-03 4.707298305108542942e-06
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1.450000000000000000e+03 1.000000000000000000e+02 5.968025329893862294e-03 4.095125006656815022e-06
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||||||
1.500000000000000000e+03 1.000000000000000000e+02 6.213281070158700216e-03 3.889082708959310676e-06
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1.500000000000000000e+03 1.000000000000000000e+02 6.151023449783679306e-03 3.407747082563892605e-06
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||||||
1.550000000000000000e+03 1.000000000000000000e+02 6.416867010266287530e-03 4.638102688188746169e-06
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1.550000000000000000e+03 1.000000000000000000e+02 6.356123279838357329e-03 4.334850576434356020e-06
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||||||
1.600000000000000000e+03 1.000000000000000000e+02 6.624721089683589630e-03 4.509133312932078743e-06
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1.600000000000000000e+03 1.000000000000000000e+02 6.551939030287030376e-03 7.048852016318317495e-06
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||||||
1.650000000000000000e+03 1.000000000000000000e+02 6.814689020393416632e-03 4.793786070736816416e-06
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1.650000000000000000e+03 1.000000000000000000e+02 6.731890990122338207e-03 4.100646159561067437e-06
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||||||
1.700000000000000000e+03 1.000000000000000000e+02 7.037005909878644473e-03 5.124697637943799726e-06
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1.700000000000000000e+03 1.000000000000000000e+02 6.941937389819940671e-03 4.995649399597023781e-06
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||||||
1.750000000000000000e+03 1.000000000000000000e+02 7.210767660362762607e-03 4.760780065895519881e-06
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1.750000000000000000e+03 1.000000000000000000e+02 7.130723769987526266e-03 4.372119755620564940e-06
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||||||
1.800000000000000000e+03 1.000000000000000000e+02 7.432531049926183013e-03 5.234147437018776624e-06
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1.800000000000000000e+03 1.000000000000000000e+02 7.331140879796293856e-03 4.733622553888157934e-06
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||||||
1.850000000000000000e+03 1.000000000000000000e+02 7.624391329954960383e-03 5.747345457474983097e-06
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1.850000000000000000e+03 1.000000000000000000e+02 7.533132550015579632e-03 5.116139233385662931e-06
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||||||
1.900000000000000000e+03 1.000000000000000000e+02 7.847124330291990399e-03 6.140559741944434632e-06
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1.900000000000000000e+03 1.000000000000000000e+02 7.722383079999417879e-03 5.538888471347748345e-06
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||||||
1.950000000000000000e+03 1.000000000000000000e+02 8.027807329854112425e-03 5.872308452137434737e-06
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1.950000000000000000e+03 1.000000000000000000e+02 7.929849700158229237e-03 4.972503192539554874e-06
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||||||
2.000000000000000000e+03 1.000000000000000000e+02 8.234741139749529940e-03 5.484516606744467484e-06
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2.000000000000000000e+03 1.000000000000000000e+02 8.121096709764970278e-03 5.661030371967715493e-06
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||||||
2.050000000000000000e+03 1.000000000000000000e+02 8.420041370336549258e-03 6.201006014692581159e-06
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2.050000000000000000e+03 1.000000000000000000e+02 8.322382019941869033e-03 5.382250019427536677e-06
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||||||
2.100000000000000000e+03 1.000000000000000000e+02 8.642610429960768600e-03 5.556879864096533714e-06
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2.100000000000000000e+03 1.000000000000000000e+02 8.517122190096416273e-03 5.625107381070845126e-06
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||||||
2.150000000000000000e+03 1.000000000000000000e+02 8.832601509420784022e-03 5.994991064019156469e-06
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2.150000000000000000e+03 1.000000000000000000e+02 8.716500719856412616e-03 6.102662149448616443e-06
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||||||
2.200000000000000000e+03 1.000000000000000000e+02 9.073848030093359038e-03 7.861155277991771910e-06
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2.200000000000000000e+03 1.000000000000000000e+02 8.933952380248228950e-03 6.092750330423816700e-06
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||||||
2.250000000000000000e+03 1.000000000000000000e+02 9.231382489961106191e-03 5.648926815135226905e-06
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2.250000000000000000e+03 1.000000000000000000e+02 9.122699559629837002e-03 6.809876512233571178e-06
|
||||||
2.300000000000000000e+03 1.000000000000000000e+02 9.434006350056733589e-03 6.503462248911149879e-06
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2.300000000000000000e+03 1.000000000000000000e+02 9.308247290100553351e-03 5.584932520477454584e-06
|
||||||
2.350000000000000000e+03 1.000000000000000000e+02 9.700876819915721025e-03 1.584549838007378060e-05
|
2.350000000000000000e+03 1.000000000000000000e+02 9.520456349891901968e-03 5.308699344716883628e-06
|
||||||
2.400000000000000000e+03 1.000000000000000000e+02 9.922618439886718433e-03 8.657328357419615412e-06
|
2.400000000000000000e+03 1.000000000000000000e+02 9.719488490081857127e-03 5.651934146065065715e-06
|
||||||
2.450000000000000000e+03 1.000000000000000000e+02 1.014477934950264171e-02 1.167775864563632931e-05
|
2.450000000000000000e+03 1.000000000000000000e+02 9.917835799969907870e-03 6.118419019078714142e-06
|
||||||
2.500000000000000000e+03 1.000000000000000000e+02 1.032955904047412263e-02 9.075708383822331569e-06
|
2.500000000000000000e+03 1.000000000000000000e+02 1.013053042981482653e-02 9.850550105507370965e-06
|
||||||
2.550000000000000000e+03 1.000000000000000000e+02 1.053916535020107438e-02 9.864027143291052185e-06
|
2.550000000000000000e+03 1.000000000000000000e+02 1.031325545991421642e-02 7.359933188585473559e-06
|
||||||
2.600000000000000000e+03 1.000000000000000000e+02 1.068992346969025592e-02 9.836075290728506446e-06
|
2.600000000000000000e+03 1.000000000000000000e+02 1.051238634987385068e-02 5.962180914305465779e-06
|
||||||
2.650000000000000000e+03 1.000000000000000000e+02 1.094912902008218260e-02 9.012869001425469971e-06
|
2.650000000000000000e+03 1.000000000000000000e+02 1.074106708972976700e-02 7.587020510527051451e-06
|
||||||
2.700000000000000000e+03 1.000000000000000000e+02 1.112891669981763698e-02 8.170382605883808633e-06
|
2.700000000000000000e+03 1.000000000000000000e+02 1.094498464983189451e-02 1.086042797471184508e-05
|
||||||
2.750000000000000000e+03 1.000000000000000000e+02 1.133965294000518001e-02 8.807898200629759062e-06
|
2.750000000000000000e+03 1.000000000000000000e+02 1.112320873024145833e-02 9.348830436389878298e-06
|
||||||
|
|
|
||||||
|
|
|
@ -2,12 +2,13 @@ from collections import deque
|
||||||
import matplotlib.pyplot as plt
|
import matplotlib.pyplot as plt
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import json
|
import json
|
||||||
|
from random import shuffle
|
||||||
|
|
||||||
from pyqcs import State, H, X, S, CZ, M, list_to_circuit
|
from pyqcs import State, H, X, S, CZ, M, list_to_circuit
|
||||||
from pyqcs.graph.state import GraphState
|
from pyqcs.graph.state import GraphState
|
||||||
from pyqcs.util.random_circuits import random_circuit
|
from pyqcs.util.random_circuits import random_circuit
|
||||||
|
|
||||||
from measure_circuit import execution_statistics
|
from measure_circuit import execution_statistics, measure_all
|
||||||
|
|
||||||
def S_with_extra_arg(act, i):
|
def S_with_extra_arg(act, i):
|
||||||
return S(act)
|
return S(act)
|
||||||
|
|
@ -19,20 +20,33 @@ def test_scaling_circuits(state_factory
|
||||||
, nqbits
|
, nqbits
|
||||||
, ncircuits
|
, ncircuits
|
||||||
, **kwargs):
|
, **kwargs):
|
||||||
results = deque()
|
trials = deque()
|
||||||
|
|
||||||
for ngates in range(nstart, nstop, step):
|
N = (nstop - nstart) / step
|
||||||
|
print()
|
||||||
|
for n, ngates in enumerate(range(nstart, nstop, step)):
|
||||||
|
print(f"generating test data... {int(n/N * 100)} %", end="\r", flush=True)
|
||||||
measurement_circuit = list_to_circuit([M(i) for i in range(nqbits)])
|
measurement_circuit = list_to_circuit([M(i) for i in range(nqbits)])
|
||||||
circuits = [random_circuit(nqbits, ngates, X, H, S_with_extra_arg, CZ)
|
circuits = [random_circuit(nqbits, ngates, X, H, S_with_extra_arg, CZ)
|
||||||
|
| measurement_circuit
|
||||||
for _ in range(ncircuits)]
|
for _ in range(ncircuits)]
|
||||||
state = state_factory(nqbits)
|
state = state_factory(nqbits)
|
||||||
|
for circuit in circuits:
|
||||||
|
trials.append((ngates, circuit, state))
|
||||||
|
print("generating test data... done ")
|
||||||
|
|
||||||
print("running test with", ngates, "gates on", nqbits, "qbits")
|
print("randomizing tests...", end="", flush=True)
|
||||||
|
shuffle(trials)
|
||||||
|
print(" done")
|
||||||
|
|
||||||
N, avg, std_dev = execution_statistics(circuits, state, scale=1, **kwargs)
|
results = measure_all(trials, **kwargs)
|
||||||
results.append([ngates, N, avg, std_dev])
|
N, avg, std_dev = execution_statistics(results, scale=1, **kwargs)
|
||||||
|
ngates = [[i] for i in sorted(results.keys())]
|
||||||
|
N = [[i] for i in N]
|
||||||
|
avg = [[i] for i in avg]
|
||||||
|
std_dev = [[i] for i in std_dev]
|
||||||
|
|
||||||
return np.array(results, dtype=np.double)
|
return np.hstack([ngates, N, avg, std_dev])
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
|
|
|
||||||
|
|
@ -2,12 +2,13 @@ from collections import deque
|
||||||
import matplotlib.pyplot as plt
|
import matplotlib.pyplot as plt
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import json
|
import json
|
||||||
|
from random import shuffle
|
||||||
|
|
||||||
from pyqcs import State, H, X, S, CZ, M, list_to_circuit
|
from pyqcs import State, H, X, S, CZ, M, list_to_circuit
|
||||||
from pyqcs.graph.state import GraphState
|
from pyqcs.graph.state import GraphState
|
||||||
from pyqcs.util.random_circuits import random_circuit
|
from pyqcs.util.random_circuits import random_circuit
|
||||||
|
|
||||||
from measure_circuit import execution_statistics
|
from measure_circuit import execution_statistics, measure_all
|
||||||
|
|
||||||
def S_with_extra_arg(act, i):
|
def S_with_extra_arg(act, i):
|
||||||
return S(act)
|
return S(act)
|
||||||
|
|
@ -19,21 +20,33 @@ def test_scaling_circuits(state_factory
|
||||||
, nqbits
|
, nqbits
|
||||||
, ncircuits
|
, ncircuits
|
||||||
, **kwargs):
|
, **kwargs):
|
||||||
results = deque()
|
trials = deque()
|
||||||
|
|
||||||
for ngates in range(nstart, nstop, step):
|
N = (nstop - nstart) / step
|
||||||
|
print()
|
||||||
|
for n, ngates in enumerate(range(nstart, nstop, step)):
|
||||||
|
print(f"generating test data... {int(n/N * 100)} %", end="\r", flush=True)
|
||||||
measurement_circuit = list_to_circuit([M(i) for i in range(nqbits)])
|
measurement_circuit = list_to_circuit([M(i) for i in range(nqbits)])
|
||||||
circuits = [random_circuit(nqbits, ngates, M, H, S_with_extra_arg, CZ)
|
circuits = [random_circuit(nqbits, ngates, M, H, S_with_extra_arg, CZ)
|
||||||
| measurement_circuit
|
| measurement_circuit
|
||||||
for _ in range(ncircuits)]
|
for _ in range(ncircuits)]
|
||||||
state = state_factory(nqbits)
|
state = state_factory(nqbits)
|
||||||
|
for circuit in circuits:
|
||||||
|
trials.append((ngates, circuit, state))
|
||||||
|
print("generating test data... done ")
|
||||||
|
|
||||||
print("running test with", ngates, "gates on", nqbits, "qbits")
|
print("randomizing tests...", end="", flush=True)
|
||||||
|
shuffle(trials)
|
||||||
|
print(" done")
|
||||||
|
|
||||||
N, avg, std_dev = execution_statistics(circuits, state, scale=1, **kwargs)
|
results = measure_all(trials, **kwargs)
|
||||||
results.append([ngates, N, avg, std_dev])
|
N, avg, std_dev = execution_statistics(results, scale=1, **kwargs)
|
||||||
|
ngates = [[i] for i in sorted(results.keys())]
|
||||||
|
N = [[i] for i in N]
|
||||||
|
avg = [[i] for i in avg]
|
||||||
|
std_dev = [[i] for i in std_dev]
|
||||||
|
|
||||||
return np.array(results, dtype=np.double)
|
return np.hstack([ngates, N, avg, std_dev])
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
|
|
|
||||||
|
|
@ -1,5 +1,5 @@
|
||||||
import timeit
|
import timeit
|
||||||
from collections import deque
|
from collections import deque, defaultdict
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
def measure_execution_time(circuit, state, repeat=100, **kwargs):
|
def measure_execution_time(circuit, state, repeat=100, **kwargs):
|
||||||
|
|
@ -14,17 +14,35 @@ def measure_execution_time(circuit, state, repeat=100, **kwargs):
|
||||||
results.append(stop - start)
|
results.append(stop - start)
|
||||||
return min(results)
|
return min(results)
|
||||||
|
|
||||||
|
def measure_all(input_circuits_and_states, **kwargs):
|
||||||
|
results = defaultdict(deque)
|
||||||
|
|
||||||
def execution_statistics(circuits, state, scale=1, **kwargs):
|
N = len(input_circuits_and_states)
|
||||||
results = deque()
|
for n, (key, circuit, state) in enumerate(input_circuits_and_states):
|
||||||
|
print(f"running tests... {int(n/N * 100)} %", end="\r", flush=True)
|
||||||
|
results[key].append(measure_execution_time(circuit, state, **kwargs))
|
||||||
|
print("running tests... done ")
|
||||||
|
return results
|
||||||
|
|
||||||
for circuit in circuits:
|
|
||||||
results.append(measure_execution_time(circuit, state, **kwargs))
|
|
||||||
|
|
||||||
results = np.array(results, dtype=np.double)
|
def execution_statistics(results, scale=None, **kwargs):
|
||||||
results /= scale
|
Ns = deque()
|
||||||
N = len(results)
|
avgs = deque()
|
||||||
avg = np.average(results)
|
std_devs = deque()
|
||||||
std_dev = np.std(results) / np.sqrt(N)
|
for k,v in sorted(results.items(), key=lambda x: x[0]):
|
||||||
|
result = np.array(v, dtype=np.double)
|
||||||
|
if(scale is not None):
|
||||||
|
result /= scale[k]
|
||||||
|
N = len(result)
|
||||||
|
avg = np.average(result)
|
||||||
|
std_dev = np.std(result) / np.sqrt(N)
|
||||||
|
|
||||||
return N, avg, std_dev
|
Ns.append(N)
|
||||||
|
avgs.append(avg)
|
||||||
|
std_devs.append(std_dev)
|
||||||
|
|
||||||
|
Ns = np.array(Ns).astype(np.int64)
|
||||||
|
avgs = np.array(avgs).astype(np.double)
|
||||||
|
std_devs = np.array(std_devs).astype(np.double)
|
||||||
|
|
||||||
|
return Ns, avgs, std_devs
|
||||||
|
|
|
||||||
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Loading…
Reference in New Issue
Block a user