|
| 1 | +# How good are Google's own "patch circuits" and "elided circuits" as a direct XEB approximation to full Sycamore circuits? |
| 2 | +# (Are they better than the 2019 Sycamore hardware?) |
| 3 | + |
| 4 | +import math |
| 5 | +import random |
| 6 | +import statistics |
| 7 | +import sys |
| 8 | + |
| 9 | +from collections import Counter |
| 10 | + |
| 11 | +from scipy.stats import binom |
| 12 | + |
| 13 | +from pyqrack import QrackSimulator |
| 14 | + |
| 15 | +from qiskit import QuantumCircuit |
| 16 | +from qiskit_aer.backends import AerSimulator |
| 17 | +from qiskit.quantum_info import Statevector |
| 18 | + |
| 19 | + |
| 20 | +def factor_width(width): |
| 21 | + col_len = math.floor(math.sqrt(width)) |
| 22 | + while ((width // col_len) * col_len) != width: |
| 23 | + col_len -= 1 |
| 24 | + row_len = width // col_len |
| 25 | + if col_len == 1: |
| 26 | + raise Exception("ERROR: Can't simulate prime number width!") |
| 27 | + |
| 28 | + return (row_len, col_len) |
| 29 | + |
| 30 | + |
| 31 | +def cx(sim, q1, q2): |
| 32 | + sim.cx(q1, q2) |
| 33 | + |
| 34 | + |
| 35 | +def cy(sim, q1, q2): |
| 36 | + sim.cy(q1, q2) |
| 37 | + |
| 38 | + |
| 39 | +def cz(sim, q1, q2): |
| 40 | + sim.cz(q1, q2) |
| 41 | + |
| 42 | + |
| 43 | +def acx(sim, q1, q2): |
| 44 | + sim.x(q1) |
| 45 | + sim.cx(q1, q2) |
| 46 | + sim.x(q1) |
| 47 | + |
| 48 | + |
| 49 | +def acy(sim, q1, q2): |
| 50 | + sim.x(q1) |
| 51 | + sim.cy(q1, q2) |
| 52 | + sim.x(q1) |
| 53 | + |
| 54 | + |
| 55 | +def acz(sim, q1, q2): |
| 56 | + sim.x(q1) |
| 57 | + sim.cz(q1, q2) |
| 58 | + sim.x(q1) |
| 59 | + |
| 60 | + |
| 61 | +def swap(sim, q1, q2): |
| 62 | + sim.swap(q1, q2) |
| 63 | + |
| 64 | + |
| 65 | +def iswap(sim, q1, q2): |
| 66 | + sim.swap(q1, q2) |
| 67 | + sim.cz(q1, q2) |
| 68 | + sim.s(q1) |
| 69 | + sim.s(q2) |
| 70 | + |
| 71 | + |
| 72 | +def iiswap(sim, q1, q2): |
| 73 | + sim.sdg(q2) |
| 74 | + sim.sdg(q1) |
| 75 | + sim.cz(q1, q2) |
| 76 | + sim.swap(q1, q2) |
| 77 | + |
| 78 | + |
| 79 | +def pswap(sim, q1, q2): |
| 80 | + sim.cz(q1, q2) |
| 81 | + sim.swap(q1, q2) |
| 82 | + |
| 83 | + |
| 84 | +def mswap(sim, q1, q2): |
| 85 | + sim.swap(q1, q2) |
| 86 | + sim.cz(q1, q2) |
| 87 | + |
| 88 | + |
| 89 | +def nswap(sim, q1, q2): |
| 90 | + sim.cz(q1, q2) |
| 91 | + sim.swap(q1, q2) |
| 92 | + sim.cz(q1, q2) |
| 93 | + |
| 94 | + |
| 95 | +def bench_qrack(width, depth, cycles): |
| 96 | + # This is a "nearest-neighbor" coupler random circuit. |
| 97 | + |
| 98 | + lcv_range = range(width) |
| 99 | + all_bits = list(lcv_range) |
| 100 | + |
| 101 | + # Nearest-neighbor couplers: |
| 102 | + gateSequence = [0, 3, 2, 1, 2, 1, 0, 3] |
| 103 | + two_bit_gates = swap, pswap, mswap, nswap, iswap, iiswap, cx, cy, cz, acx, acy, acz |
| 104 | + |
| 105 | + row_len, col_len = factor_width(width) |
| 106 | + |
| 107 | + rcs = QuantumCircuit(width) |
| 108 | + for d in range(depth): |
| 109 | + # Single-qubit gates |
| 110 | + for i in lcv_range: |
| 111 | + th = random.uniform(0, 2 * math.pi) |
| 112 | + ph = random.uniform(0, 2 * math.pi) |
| 113 | + lm = random.uniform(0, 2 * math.pi) |
| 114 | + rcs.u(th, ph, lm, i) |
| 115 | + |
| 116 | + # Nearest-neighbor couplers: |
| 117 | + ############################ |
| 118 | + gate = gateSequence.pop(0) |
| 119 | + gateSequence.append(gate) |
| 120 | + for row in range(1, row_len, 2): |
| 121 | + for col in range(col_len): |
| 122 | + temp_row = row |
| 123 | + temp_col = col |
| 124 | + temp_row = temp_row + (1 if (gate & 2) else -1) |
| 125 | + temp_col = temp_col + (1 if (gate & 1) else 0) |
| 126 | + |
| 127 | + if temp_row < 0: |
| 128 | + temp_row = temp_row + row_len |
| 129 | + if temp_col < 0: |
| 130 | + temp_col = temp_col + col_len |
| 131 | + if temp_row >= row_len: |
| 132 | + temp_row = temp_row - row_len |
| 133 | + if temp_col >= col_len: |
| 134 | + temp_col = temp_col - col_len |
| 135 | + |
| 136 | + b1 = row * row_len + col |
| 137 | + b2 = temp_row * row_len + temp_col |
| 138 | + |
| 139 | + if (b1 >= width) or (b2 >= width): |
| 140 | + continue |
| 141 | + |
| 142 | + g = random.choice(two_bit_gates) |
| 143 | + g(rcs, b1, b2) |
| 144 | + |
| 145 | + ops = ['I', 'X', 'Y', 'Z'] |
| 146 | + pauli_strings = [] |
| 147 | + |
| 148 | + otoc = QuantumCircuit(width) |
| 149 | + for cycle in range(cycles): |
| 150 | + otoc &= rcs |
| 151 | + string = [] |
| 152 | + for b in range(width): |
| 153 | + string.append(random.choice(ops)) |
| 154 | + pauli_strings.append("".join(string)) |
| 155 | + act_string(otoc, string) |
| 156 | + otoc &= rcs.inverse() |
| 157 | + |
| 158 | + |
| 159 | + experiment = QrackSimulator(width) |
| 160 | + experiment.run_qiskit_circuit(otoc) |
| 161 | + |
| 162 | + otoc_aer = otoc.copy() |
| 163 | + otoc_aer.save_statevector() |
| 164 | + control = AerSimulator(method="statevector") |
| 165 | + job = control.run(otoc_aer) |
| 166 | + |
| 167 | + shots = 1 << (width + 2) |
| 168 | + experiment_counts = dict(Counter(experiment.measure_shots(all_bits, shots))) |
| 169 | + control_probs = Statevector(job.result().get_statevector()).probabilities() |
| 170 | + |
| 171 | + return calc_stats(control_probs, experiment_counts, d + 1, shots), pauli_strings |
| 172 | + |
| 173 | + |
| 174 | +def act_string(otoc, string): |
| 175 | + for i in range(len(string)): |
| 176 | + match string[i]: |
| 177 | + case 'X': |
| 178 | + otoc.x(i) |
| 179 | + case 'Y': |
| 180 | + otoc.y(i) |
| 181 | + case 'Z': |
| 182 | + otoc.z(i) |
| 183 | + case _: |
| 184 | + pass |
| 185 | + |
| 186 | + |
| 187 | +def calc_stats(ideal_probs, counts, depth, shots): |
| 188 | + # For QV, we compare probabilities of (ideal) "heavy outputs." |
| 189 | + # If the probability is above 2/3, the protocol certifies/passes the qubit width. |
| 190 | + n_pow = len(ideal_probs) |
| 191 | + n = int(round(math.log2(n_pow))) |
| 192 | + threshold = statistics.median(ideal_probs) |
| 193 | + u_u = statistics.mean(ideal_probs) |
| 194 | + numer = 0 |
| 195 | + denom = 0 |
| 196 | + sum_hog_counts = 0 |
| 197 | + for i in range(n_pow): |
| 198 | + count = counts[i] if i in counts else 0 |
| 199 | + ideal = ideal_probs[i] |
| 200 | + |
| 201 | + # XEB / EPLG |
| 202 | + denom += (ideal - u_u) ** 2 |
| 203 | + numer += (ideal - u_u) * ((count / shots) - u_u) |
| 204 | + |
| 205 | + # QV / HOG |
| 206 | + if ideal > threshold: |
| 207 | + sum_hog_counts += count |
| 208 | + |
| 209 | + hog_prob = sum_hog_counts / shots |
| 210 | + xeb = numer / denom |
| 211 | + # p-value of heavy output count, if method were actually 50/50 chance of guessing |
| 212 | + p_val = ( |
| 213 | + (1 - binom.cdf(sum_hog_counts - 1, shots, 1 / 2)) if sum_hog_counts > 0 else 1 |
| 214 | + ) |
| 215 | + |
| 216 | + return { |
| 217 | + "qubits": n, |
| 218 | + "depth": depth, |
| 219 | + "xeb": float(xeb), |
| 220 | + "hog_prob": float(hog_prob), |
| 221 | + "p-value": float(p_val), |
| 222 | + } |
| 223 | + |
| 224 | + |
| 225 | +def main(): |
| 226 | + if len(sys.argv) < 4: |
| 227 | + raise RuntimeError( |
| 228 | + "Usage: python3 fc_qiskit_validation.py [width] [depth] [cycles]" |
| 229 | + ) |
| 230 | + |
| 231 | + width = int(sys.argv[1]) |
| 232 | + depth = int(sys.argv[2]) |
| 233 | + cycles = int(sys.argv[3]) |
| 234 | + |
| 235 | + # Run the benchmarks |
| 236 | + print(bench_qrack(width, depth, cycles)) |
| 237 | + |
| 238 | + return 0 |
| 239 | + |
| 240 | + |
| 241 | +if __name__ == "__main__": |
| 242 | + sys.exit(main()) |
0 commit comments