some work on performance stuff
This commit is contained in:
parent
0a0f424b98
commit
0cd2dfa8b4
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@ -3,7 +3,7 @@ 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 pyqcs import State, H, X, S, CZ
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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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@ -19,9 +19,13 @@ def test_scaling_qbits(state_factory
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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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results = deque()
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for qbits in range(nstart, nstop):
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for qbits in range(nstart, nstop):
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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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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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print("running test with", qbits, "qbits")
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print("running test with", qbits, "qbits")
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@ -1,12 +1,12 @@
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@ -1,12 +1,12 @@
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@ -1,48 +1,48 @@
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48
performance/regimes/circuit_scaling_graph0_measurements.csv
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48
performance/regimes/circuit_scaling_graph0_measurements.csv
Normal file
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@ -0,0 +1,48 @@
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48
performance/regimes/circuit_scaling_graph1_measurements.csv
Normal file
48
performance/regimes/circuit_scaling_graph1_measurements.csv
Normal file
|
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@ -0,0 +1,48 @@
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||||||
|
1.600000000000000000e+03 1.000000000000000000e+02 6.624721089683589630e-03 4.509133312932078743e-06
|
||||||
|
1.650000000000000000e+03 1.000000000000000000e+02 6.814689020393416632e-03 4.793786070736816416e-06
|
||||||
|
1.700000000000000000e+03 1.000000000000000000e+02 7.037005909878644473e-03 5.124697637943799726e-06
|
||||||
|
1.750000000000000000e+03 1.000000000000000000e+02 7.210767660362762607e-03 4.760780065895519881e-06
|
||||||
|
1.800000000000000000e+03 1.000000000000000000e+02 7.432531049926183013e-03 5.234147437018776624e-06
|
||||||
|
1.850000000000000000e+03 1.000000000000000000e+02 7.624391329954960383e-03 5.747345457474983097e-06
|
||||||
|
1.900000000000000000e+03 1.000000000000000000e+02 7.847124330291990399e-03 6.140559741944434632e-06
|
||||||
|
1.950000000000000000e+03 1.000000000000000000e+02 8.027807329854112425e-03 5.872308452137434737e-06
|
||||||
|
2.000000000000000000e+03 1.000000000000000000e+02 8.234741139749529940e-03 5.484516606744467484e-06
|
||||||
|
2.050000000000000000e+03 1.000000000000000000e+02 8.420041370336549258e-03 6.201006014692581159e-06
|
||||||
|
2.100000000000000000e+03 1.000000000000000000e+02 8.642610429960768600e-03 5.556879864096533714e-06
|
||||||
|
2.150000000000000000e+03 1.000000000000000000e+02 8.832601509420784022e-03 5.994991064019156469e-06
|
||||||
|
2.200000000000000000e+03 1.000000000000000000e+02 9.073848030093359038e-03 7.861155277991771910e-06
|
||||||
|
2.250000000000000000e+03 1.000000000000000000e+02 9.231382489961106191e-03 5.648926815135226905e-06
|
||||||
|
2.300000000000000000e+03 1.000000000000000000e+02 9.434006350056733589e-03 6.503462248911149879e-06
|
||||||
|
2.350000000000000000e+03 1.000000000000000000e+02 9.700876819915721025e-03 1.584549838007378060e-05
|
||||||
|
2.400000000000000000e+03 1.000000000000000000e+02 9.922618439886718433e-03 8.657328357419615412e-06
|
||||||
|
2.450000000000000000e+03 1.000000000000000000e+02 1.014477934950264171e-02 1.167775864563632931e-05
|
||||||
|
2.500000000000000000e+03 1.000000000000000000e+02 1.032955904047412263e-02 9.075708383822331569e-06
|
||||||
|
2.550000000000000000e+03 1.000000000000000000e+02 1.053916535020107438e-02 9.864027143291052185e-06
|
||||||
|
2.600000000000000000e+03 1.000000000000000000e+02 1.068992346969025592e-02 9.836075290728506446e-06
|
||||||
|
2.650000000000000000e+03 1.000000000000000000e+02 1.094912902008218260e-02 9.012869001425469971e-06
|
||||||
|
2.700000000000000000e+03 1.000000000000000000e+02 1.112891669981763698e-02 8.170382605883808633e-06
|
||||||
|
2.750000000000000000e+03 1.000000000000000000e+02 1.133965294000518001e-02 8.807898200629759062e-06
|
||||||
|
|
|
@ -0,0 +1 @@
|
||||||
|
{"nstart": 400, "nstop": 2800, "step": 50, "ncircuits": 100, "nqbits0": 100, "nqbits1": 50, "seed": 3735928559}
|
||||||
|
|
@ -1 +1 @@
|
||||||
{"nstart": 400, "nstop": 1800, "step": 50, "ncircuits": 50, "nqbits0": 100, "nqbits1": 50, "seed": 3735928559}
|
{"nstart": 400, "nstop": 2800, "step": 50, "ncircuits": 100, "nqbits0": 100, "nqbits1": 50, "seed": 3735928559}
|
||||||
|
|
@ -3,7 +3,7 @@ import matplotlib.pyplot as plt
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import json
|
import json
|
||||||
|
|
||||||
from pyqcs import State, H, X, S, CZ
|
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
|
||||||
|
|
||||||
|
|
@ -22,6 +22,7 @@ def test_scaling_circuits(state_factory
|
||||||
results = deque()
|
results = deque()
|
||||||
|
|
||||||
for ngates in range(nstart, nstop, step):
|
for ngates in range(nstart, nstop, step):
|
||||||
|
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)
|
||||||
for _ in range(ncircuits)]
|
for _ in range(ncircuits)]
|
||||||
state = state_factory(nqbits)
|
state = state_factory(nqbits)
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,82 @@
|
||||||
|
from collections import deque
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import numpy as np
|
||||||
|
import json
|
||||||
|
|
||||||
|
from pyqcs import State, H, X, S, CZ, M, list_to_circuit
|
||||||
|
from pyqcs.graph.state import GraphState
|
||||||
|
from pyqcs.util.random_circuits import random_circuit
|
||||||
|
|
||||||
|
from measure_circuit import execution_statistics
|
||||||
|
|
||||||
|
def S_with_extra_arg(act, i):
|
||||||
|
return S(act)
|
||||||
|
|
||||||
|
def test_scaling_circuits(state_factory
|
||||||
|
, nstart
|
||||||
|
, nstop
|
||||||
|
, step
|
||||||
|
, nqbits
|
||||||
|
, ncircuits
|
||||||
|
, **kwargs):
|
||||||
|
results = deque()
|
||||||
|
|
||||||
|
for ngates in range(nstart, nstop, step):
|
||||||
|
measurement_circuit = list_to_circuit([M(i) for i in range(nqbits)])
|
||||||
|
circuits = [random_circuit(nqbits, ngates, M, H, S_with_extra_arg, CZ)
|
||||||
|
| measurement_circuit
|
||||||
|
for _ in range(ncircuits)]
|
||||||
|
state = state_factory(nqbits)
|
||||||
|
|
||||||
|
print("running test with", ngates, "gates on", nqbits, "qbits")
|
||||||
|
|
||||||
|
N, avg, std_dev = execution_statistics(circuits, state, scale=1, **kwargs)
|
||||||
|
results.append([ngates, N, avg, std_dev])
|
||||||
|
|
||||||
|
return np.array(results, dtype=np.double)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
nstart = 400
|
||||||
|
nstop = 2800
|
||||||
|
step = 50
|
||||||
|
ncircuits = 100
|
||||||
|
nqbits0 = 100
|
||||||
|
nqbits1 = 50
|
||||||
|
seed = 0xdeadbeef
|
||||||
|
|
||||||
|
np.random.seed(seed)
|
||||||
|
results_graph0 = test_scaling_circuits(GraphState.new_zero_state
|
||||||
|
, nstart
|
||||||
|
, nstop
|
||||||
|
, step
|
||||||
|
, nqbits0
|
||||||
|
, ncircuits
|
||||||
|
, repeat=10)
|
||||||
|
np.random.seed(seed)
|
||||||
|
results_graph1 = test_scaling_circuits(GraphState.new_zero_state
|
||||||
|
, nstart
|
||||||
|
, nstop
|
||||||
|
, step
|
||||||
|
, nqbits1
|
||||||
|
, ncircuits
|
||||||
|
, repeat=10)
|
||||||
|
|
||||||
|
np.savetxt("circuit_scaling_graph0_measurements.csv", results_graph0)
|
||||||
|
print("saved results0 to circuit_scaling_graph0_measurements.csv")
|
||||||
|
np.savetxt("circuit_scaling_graph1_measurements.csv", results_graph1)
|
||||||
|
print("saved results1 to circuit_scaling_graph1_measurements.csv")
|
||||||
|
|
||||||
|
meta = {
|
||||||
|
"nstart": nstart
|
||||||
|
, "nstop": nstop
|
||||||
|
, "step": step
|
||||||
|
, "ncircuits": ncircuits
|
||||||
|
, "nqbits0": nqbits0
|
||||||
|
, "nqbits1": nqbits1
|
||||||
|
, "seed": seed}
|
||||||
|
|
||||||
|
with open("circuit_scaling_measurements_meta.json", "w") as fout:
|
||||||
|
json.dump(meta, fout)
|
||||||
|
print("saved meta to circuit_scaling_measurements_meta.json")
|
||||||
|
|
||||||
|
|
@ -0,0 +1,29 @@
|
||||||
|
from collections import deque
|
||||||
|
import matplotlib
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import numpy as np
|
||||||
|
import json
|
||||||
|
|
||||||
|
matplotlib.rcParams.update({'errorbar.capsize': 2})
|
||||||
|
|
||||||
|
results_graph0 = np.genfromtxt("circuit_scaling_graph0_measurements.csv")
|
||||||
|
results_graph1 = np.genfromtxt("circuit_scaling_graph1_measurements.csv")
|
||||||
|
with open("circuit_scaling_measurements_meta.json") as fin:
|
||||||
|
meta = json.load(fin)
|
||||||
|
|
||||||
|
h0 = plt.errorbar(results_graph0[:, 0], results_graph0[:, 2], results_graph0[:, 3]
|
||||||
|
, label=f"Graphical Simulator $N_q={meta['nqbits0']}$ Qbits"
|
||||||
|
, marker="^"
|
||||||
|
, color="black")
|
||||||
|
h1 = plt.errorbar(results_graph1[:, 0], results_graph1[:, 2], results_graph1[:, 3]
|
||||||
|
, label=f"Graphical Simulator $N_q={meta['nqbits1']}$ Qbits"
|
||||||
|
, marker="o"
|
||||||
|
, color="black")
|
||||||
|
|
||||||
|
plt.legend(handles=[h0, h1])
|
||||||
|
plt.xlabel("Number of gates in circuit")
|
||||||
|
plt.ylabel("Execution time per circuit [s]")
|
||||||
|
plt.title(f"Execution Time for Random Circuits with Random Measurements")
|
||||||
|
|
||||||
|
plt.savefig("scaling_circuits_measurements_linear.png", dpi=400)
|
||||||
|
plt.show()
|
||||||
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performance/regimes/scaling_circuits_measurements_linear.png
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performance/regimes/scaling_circuits_measurements_linear.png
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Loading…
Reference in New Issue
Block a user