Clean ALL THE THINGS!

clifford_rz_mirror.py

@@ -203,7 +203,7 @@ def main():
 
     # Run the benchmarks
     results = []
-    for i in range(samples):
+    for _ in range(samples):
         results.append(bench_qrack(qubits, magic))
 
     r_magic = sum(r[0] for r in results) / samples

clifford_rz_validation.py

@@ -205,7 +205,7 @@ def main():
         width_results = []
 
         # Run the benchmarks
-        for i in range(samples):
+        for _ in range(samples):
             width_results.append(bench_qrack(n))
 
         inner_product_result = sum(r for r in width_results) / samples

entanglement-breaking.py

@@ -9,8 +9,6 @@
 
 def calc_xeb(ideal_probs, split_probs):
     n_pow = len(ideal_probs)
-    n = int(round(math.log2(n_pow)))
-    threshold = statistics.median(ideal_probs)
     u_u = statistics.mean(ideal_probs)
     numer = 0
     denom = 0

ghz_nc.py

@@ -13,11 +13,6 @@ def bench_qrack(n):
 
     sim = QrackSimulator(n, isTensorNetwork=False, isSchmidtDecompose=False, isStabilizerHybrid=True)
 
-    lcv_range = range(n)
-    all_bits = list(lcv_range)
-
-    single_count = 0
-    double_count = 0
     sim.h(0)
     for q in range(n - 1):
         sim.mcx([q], q + 1)
@@ -43,7 +38,7 @@ def main():
         width_results = []
 
         # Run the benchmarks
-        for i in range(samples):
+        for _ in range(samples):
             width_results.append(bench_qrack(n))
 
         time_result = sum(r[0] for r in width_results) / samples

ghz_qbdd.py

@@ -14,11 +14,6 @@ def bench_qrack(n):
 
     sim = QrackSimulator(n, isSchmidtDecompose=False, isBinaryDecisionTree=True)
 
-    lcv_range = range(n)
-    all_bits = list(lcv_range)
-
-    single_count = 0
-    double_count = 0
     sim.h(0)
     for q in range(n - 1):
         sim.mcx([q], q + 1)

marp_2d.py

@@ -65,7 +65,6 @@ def bench_qrack(width, depth, sdrp_samples):
     start = time.perf_counter()
 
     lcv_range = range(width)
-    all_bits = list(lcv_range)
 
     # Nearest-neighbor couplers:
     gateSequence = [ 0, 3, 2, 1, 2, 1, 0, 3 ]

marp_full.py

@@ -68,11 +68,6 @@ def bench_qrack(width, depth, sdrp_samples):
 
     two_bit_gates = swap, pswap, mswap, nswap, iswap, iiswap, cx, cy, cz, acx, acy, acz
 
-    col_len = math.floor(math.sqrt(width))
-    while (((width // col_len) * col_len) != width):
-        col_len -= 1
-    row_len = width // col_len
-
     sdrp_segments = sdrp_samples - 1
     for i in range(0, sdrp_samples):
         start = time.perf_counter()

mirror_nn.py

@@ -12,7 +12,6 @@ def bench_qrack(n):
     circ = QrackCircuit()
 
     lcv_range = range(n)
-    all_bits = list(lcv_range)
     x_op = [0, 1, 1, 0]
     gateSequence = [ 0, 3, 2, 1, 2, 1, 0, 3 ]
     row_len = math.ceil(math.sqrt(n))

mirror_qv.py

@@ -14,17 +14,15 @@ def bench_qrack(n):
     lcv_range = range(n)
     all_bits = list(lcv_range)
     x_op = [0, 1, 1, 0]
-    gateSequence = [ 0, 3, 2, 1, 2, 1, 0, 3 ]
-    row_len = math.ceil(math.sqrt(n))
 
     for _ in lcv_range:
         # Single-qubit gates
         for i in lcv_range:
             th = random.uniform(0, 2 * math.pi)
             ph = random.uniform(0, 2 * math.pi)
             lm = random.uniform(0, 2 * math.pi)
-            cos0 = math.cos(th / 2);
-            sin0 = math.sin(th / 2);
+            cos0 = math.cos(th / 2)
+            sin0 = math.sin(th / 2)
             u_op = [
                 cos0 + 0j, sin0 * (-math.cos(lm) + -math.sin(lm) * 1j),
                 sin0 * (math.cos(ph) + math.sin(ph) * 1j), cos0 * (math.cos(ph + lm) + math.sin(ph + lm) * 1j)

nearest_clifford_circuit.py

@@ -80,7 +80,7 @@ def main():
         width_results = []
 
         # Run the benchmarks
-        for i in range(samples):
+        for _ in range(samples):
             width_results.append(bench_qrack(n))
             print(width_results[-1][1])
 

qbdd/qbdd_fc.py

@@ -1,7 +1,6 @@
 # Demonstrates the use of "Quantum Binary Decision Diagram (QBDD) rounding parameter" ("QBDDRP")
 
 import math
-import os
 import random
 import sys
 import time

qbdd/qbdd_fc_fidelity.py

@@ -1,10 +1,8 @@
 # Demonstrates the use of "Quantum Binary Decision Diagram (QBDD) rounding parameter" ("QBDDRP")
 
 import math
-import os
 import random
 import sys
-import time
 
 import numpy as np
 
@@ -22,7 +20,6 @@ def bench_qrack(width, depth):
     gate_count = 0
 
     for d in range(depth):
-        start = time.perf_counter()
         # Single-qubit gates
         for i in lcv_range:
             th = random.uniform(0, 2 * math.pi)

qbdd/qbdd_fc_fidelity_qiskit.py

@@ -1,10 +1,8 @@
 # Demonstrates the use of "Quantum Binary Decision Diagram (QBDD) rounding parameter" ("QBDDRP")
 
 import math
-import os
 import random
 import sys
-import time
 
 import numpy as np
 
@@ -39,7 +37,6 @@ def bench_qrack(width, depth):
 
     for d in range(depth):
         experiment.reset_all()
-        start = time.perf_counter()
         # Single-qubit gates
         for i in lcv_range:
             rand_u3(circ, i)

qbdd/qbdd_fc_parallel.py

@@ -2,7 +2,6 @@
 
 import math
 import multiprocessing
-import os
 import random
 import sys
 import time
@@ -56,7 +55,7 @@ def main():
 
     # Run the benchmarks
     pool = multiprocessing.Pool(processes = multiprocessing.cpu_count())
-    result = pool.map(bench_qrack, [(width, depth)] * shots)
+    pool.map(bench_qrack, [(width, depth)] * shots)
     pool.close()
 
     return 0

qbdd/qbdd_nc_fc.py

@@ -1,7 +1,6 @@
 # Demonstrates the use of "Quantum Binary Decision Diagram (QBDD) rounding parameter" ("QBDDRP")
 
 import math
-import os
 import random
 import sys
 import time

qbdd/qbdd_nc_fc_fidelity.py

@@ -1,10 +1,8 @@
 # Demonstrates the use of "Quantum Binary Decision Diagram (QBDD) rounding parameter" ("QBDDRP")
 
 import math
-import os
 import random
 import sys
-import time
 
 import numpy as np
 
@@ -24,7 +22,6 @@ def bench_qrack(width, depth):
     gate_count = 0
 
     for d in range(depth):
-        start = time.perf_counter()
         # Single-qubit gates
         for i in lcv_range:
             th = random.uniform(0, 2 * math.pi)

qbdd/qbdd_nc_fc_fidelity_qiskit.py

@@ -1,17 +1,14 @@
 # Demonstrates the use of "Quantum Binary Decision Diagram (QBDD) rounding parameter" ("QBDDRP")
 
 import math
-import os
 import random
 import sys
-import time
 
 import numpy as np
 
-from pyqrack import QrackSimulator, Pauli
+from pyqrack import QrackSimulator
 
 from qiskit import QuantumCircuit
-from qiskit.compiler import transpile
 from qiskit_aer.backends import AerSimulator
 
 

qbdd/qbdd_nc_fc_parallel.py

@@ -2,7 +2,6 @@
 
 import math
 import multiprocessing
-import os
 import random
 import sys
 import time
@@ -62,7 +61,7 @@ def main():
 
     # Run the benchmarks
     pool = multiprocessing.Pool(processes = multiprocessing.cpu_count())
-    result = pool.map(bench_qrack, [(width, depth)] * shots)
+    pool.map(bench_qrack, [(width, depth)] * shots)
     pool.close()
 
     return 0

qbdd/qbdd_nc_nn_odd_orbifold.py

@@ -1,7 +1,6 @@
 # Demonstrates the use of "Quantum Binary Decision Diagram" (QBDD) and QBDD rounding parameter (QBDDRP) with near-Clifford (nearest-neighbor)
 
 import math
-import os
 import random
 import sys
 import time
@@ -80,7 +79,6 @@ def bench_qrack(width, depth):
     # sim.set_reactive_separate(True)
 
     lcv_range = range(width)
-    all_bits = list(lcv_range)
 
     # Nearest-neighbor couplers:
     gateSequence = [ 0, 3, 2, 1, 2, 1, 0, 3 ]

qbdd/qbdd_nc_nn_odd_orbifold_fidelity.py

@@ -1,10 +1,8 @@
 # Validates the use of "Quantum Binary Decision Diagram" (QBDD) with light-cone (nearest-neighbor, orbifolded)
 
 import math
-import os
 import random
 import sys
-import time
 
 import numpy as np
 
@@ -92,7 +90,6 @@ def bench_qrack(width, depth):
     control = QrackSimulator(width)
 
     lcv_range = range(width)
-    all_bits = list(lcv_range)
 
     # Nearest-neighbor couplers:
     gateSequence = [ 0, 3, 2, 1, 2, 1, 0, 3 ]
@@ -112,7 +109,6 @@ def bench_qrack(width, depth):
     gate_count = 0
 
     for d in range(depth):
-        start = time.perf_counter()
         # Single-qubit gates
         for i in lcv_range:
             for _ in range(3):

qbdd/qbdd_nc_nn_odd_orbifold_fidelity_qiskit.py

@@ -1,14 +1,12 @@
 # Validates the use of "Quantum Binary Decision Diagram" (QBDD) with light-cone (nearest-neighbor, orbifolded)
 
 import math
-import os
 import random
 import sys
-import time
 
 import numpy as np
 
-from pyqrack import QrackSimulator, Pauli
+from pyqrack import QrackSimulator
 
 from qiskit import QuantumCircuit
 from qiskit.compiler import transpile
@@ -105,7 +103,6 @@ def bench_qrack(width, depth):
     control = AerSimulator(method="statevector")
 
     lcv_range = range(width)
-    all_bits = list(lcv_range)
 
     # Nearest-neighbor couplers:
     gateSequence = [ 0, 3, 2, 1, 2, 1, 0, 3 ]
@@ -126,7 +123,6 @@ def bench_qrack(width, depth):
 
     for d in range(depth):
         experiment.reset_all()
-        start = time.perf_counter()
         # Single-qubit gates
         for i in lcv_range:
             rand_u3(circ, i)

qbdd/qbdd_nc_nn_odd_orbifold_parallel.py

@@ -2,7 +2,6 @@
 
 import math
 import multiprocessing
-import os
 import random
 import sys
 import time
@@ -83,7 +82,6 @@ def bench_qrack(width_depth):
     # sim.set_reactive_separate(True)
 
     lcv_range = range(width)
-    all_bits = list(lcv_range)
 
     # Nearest-neighbor couplers:
     gateSequence = [ 0, 3, 2, 1, 2, 1, 0, 3 ]
@@ -156,7 +154,7 @@ def main():
 
     # Run the benchmarks
     pool = multiprocessing.Pool(processes = multiprocessing.cpu_count())
-    result = pool.map(bench_qrack, [(width, depth)] * shots)
+    pool.map(bench_qrack, [(width, depth)] * shots)
     pool.close()
 
     return 0

qbdd/qbdd_nn.py

@@ -1,7 +1,6 @@
 # Demonstrates the use of "Quantum Binary Decision Diagram" (QBDD) and QBDD rounding parameter ("QBDDRP") with light-cone (nearest neighbor)
 
 import math
-import os
 import random
 import sys
 import time
@@ -68,7 +67,6 @@ def bench_qrack(width, depth):
     sim = QrackSimulator(width, isBinaryDecisionTree=True)
 
     lcv_range = range(width)
-    all_bits = list(lcv_range)
 
     # Nearest-neighbor couplers:
     gateSequence = [ 0, 3, 2, 1, 2, 1, 0, 3 ]

qbdd/qbdd_nn_fidelity.py

@@ -1,10 +1,8 @@
 # Validates the use of "Quantum Binary Decision Diagram" (QBDD) and QBDD rounding parameter ("QBDDRP") with light-cone (nearest-neighbor)
 
 import math
-import os
 import random
 import sys
-import time
 
 import numpy as np
 
@@ -82,7 +80,6 @@ def bench_qrack(width, depth):
     control = QrackSimulator(width)
 
     lcv_range = range(width)
-    all_bits = list(lcv_range)
 
     # Nearest-neighbor couplers:
     gateSequence = [ 0, 3, 2, 1, 2, 1, 0, 3 ]
@@ -99,7 +96,6 @@ def bench_qrack(width, depth):
     gate_count = 0
 
     for d in range(depth):
-        start = time.perf_counter()
         # Single-qubit gates
         for i in lcv_range:
             th = random.uniform(0, 2 * math.pi)