Working on benchmarking

benchmarking.py

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+# Reporting Imports
+import time
+import random
+import statistics
+
+# QCGPU Implementation
+import qcgpu
+
+def bench_qcgpu(n, depth):
+    h = qcgpu.gate.h()
+    x = qcgpu.gate.x()
+    t = qcgpu.gate.t()
+
+    state = qcgpu.State(n)
+
+    start = time.time()
+
+    for level in range(depth):
+        for q in range(n):
+    
+            state.apply_gate(h, q)
+            state.apply_gate(t, q)
+
+            if q != 0:
+                state.apply_controlled_gate(x, q, 0)
+    return time.time() - start
+
+# Qiskit Implementation
+from qiskit import ClassicalRegister, QuantumRegister
+from qiskit import QuantumCircuit, execute
+
+def bench_qiskit(n, depth):
+    q = QuantumRegister(n)
+    c = ClassicalRegister(n)
+    qc = QuantumCircuit(q, c)
+    
+    for level in range(depth):
+        for i in range(n):
+            qc.h(q[i])
+            qc.t(q[i])
+
+            if i != 0:
+                qc.cx(q[i], q[0])
+    qc.measure(q, c)
+
+    start = time.time()
+    
+    job_sim = execute(qc, "local_statevector_simulator")
+    
+    return time.time() - start
+
+# ProjectQ Implementation
+from projectq import MainEngine
+import projectq.ops as ops
+from projectq.backends import Simulator
+from projectq.types import Qureg
+
+def bench_projectq(n, depth):
+    eng = MainEngine(backend=Simulator(gate_fusion=True), engine_list=[])
+    qbits = eng.allocate_qureg(n)
+
+    start = time.time()
+
+    for level in range(depth):
+        for q in qbits:
+            ops.H | q
+            ops.T | q
+            if q != qbits[0]:
+                ops.CNOT | (q, qbits[0])
+
+    runtime = time.time() - start
+
+    for q in qbits:
+        ops.Measure | q
+    eng.flush()
+    return runtime
+
+# Reporting
+
+functions = bench_qcgpu, bench_qiskit, bench_projectq
+
+times = {f.__name__: [] for f in functions}
+
+names = []
+means = []
+
+samples = 10
+for i in range(samples):  # adjust accordingly so whole thing takes a few sec
+    progress = i / samples
+    print("\rProgress: [{0:50s}] {1:.1f}%".format('#' * int(progress * 50), progress*100), end="", flush=True)
+    func = random.choice(functions)
+    t = func(5,1)
+    times[func.__name__].append(t)
+
+print('')
+
+for name, numbers in times.items():
+    print('FUNCTION:', name, 'Used', len(numbers), 'times')
+    print('\tMEDIAN', statistics.median(numbers))
+    print('\tMEAN  ', statistics.mean(numbers))
+    print('\tSTDEV ', statistics.stdev(numbers))
+    means.append(statistics.mean(numbers))
+    names.append(name)
+
+# Graphing
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+index = np.arange(len(names))
+print(index)
+
+plt.bar(index, means)
+plt.xlabel('Function')
+plt.ylabel('Time (s)')
+plt.xticks(index, names)
+plt.title('Performance')
+plt.show()