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| 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 operator |
| 6 | +import random |
| 7 | +import statistics |
| 8 | +import sys |
| 9 | + |
| 10 | +from collections import Counter |
| 11 | + |
| 12 | +from scipy.stats import binom |
| 13 | + |
| 14 | +from pyqrack import QrackSimulator |
| 15 | + |
| 16 | +from qiskit import QuantumCircuit |
| 17 | +from qiskit_aer.backends import AerSimulator |
| 18 | +from qiskit.quantum_info import Statevector |
| 19 | + |
| 20 | +import quimb.tensor as tn |
| 21 | +from qiskit_quimb import quimb_circuit |
| 22 | + |
| 23 | + |
| 24 | +# Function by Google search AI |
| 25 | +def int_to_bitstring(integer, length): |
| 26 | + return (bin(integer)[2:].zfill(length))[::-1] |
| 27 | + |
| 28 | + |
| 29 | +def factor_width(width): |
| 30 | + col_len = math.floor(math.sqrt(width)) |
| 31 | + while ((width // col_len) * col_len) != width: |
| 32 | + col_len -= 1 |
| 33 | + row_len = width // col_len |
| 34 | + if col_len == 1: |
| 35 | + raise Exception("ERROR: Can't simulate prime number width!") |
| 36 | + |
| 37 | + return (row_len, col_len) |
| 38 | + |
| 39 | + |
| 40 | +def cx(sim, q1, q2): |
| 41 | + sim.cx(q1, q2) |
| 42 | + |
| 43 | + |
| 44 | +def cy(sim, q1, q2): |
| 45 | + sim.cy(q1, q2) |
| 46 | + |
| 47 | + |
| 48 | +def cz(sim, q1, q2): |
| 49 | + sim.cz(q1, q2) |
| 50 | + |
| 51 | + |
| 52 | +def acx(sim, q1, q2): |
| 53 | + sim.x(q1) |
| 54 | + sim.cx(q1, q2) |
| 55 | + sim.x(q1) |
| 56 | + |
| 57 | + |
| 58 | +def acy(sim, q1, q2): |
| 59 | + sim.x(q1) |
| 60 | + sim.cy(q1, q2) |
| 61 | + sim.x(q1) |
| 62 | + |
| 63 | + |
| 64 | +def acz(sim, q1, q2): |
| 65 | + sim.x(q1) |
| 66 | + sim.cz(q1, q2) |
| 67 | + sim.x(q1) |
| 68 | + |
| 69 | + |
| 70 | +def bench_qrack(width, depth, sdrp): |
| 71 | + lcv_range = range(width) |
| 72 | + all_bits = list(lcv_range) |
| 73 | + retained = width * width |
| 74 | + shots = retained * width |
| 75 | + gateSequence = [0, 3, 2, 1, 2, 1, 0, 3] |
| 76 | + two_bit_gates = cx, cy, cz, acx, acy, acz |
| 77 | + row_len, col_len = factor_width(width) |
| 78 | + |
| 79 | + rcs = QuantumCircuit(width) |
| 80 | + for d in range(depth): |
| 81 | + # Single-qubit gates |
| 82 | + for i in lcv_range: |
| 83 | + for b in range(3): |
| 84 | + rcs.h(i) |
| 85 | + rcs.rz(random.uniform(0, 2 * math.pi), i) |
| 86 | + |
| 87 | + # Nearest-neighbor couplers: |
| 88 | + ############################ |
| 89 | + gate = gateSequence.pop(0) |
| 90 | + gateSequence.append(gate) |
| 91 | + for row in range(1, row_len, 2): |
| 92 | + for col in range(col_len): |
| 93 | + temp_row = row |
| 94 | + temp_col = col |
| 95 | + temp_row = temp_row + (1 if (gate & 2) else -1) |
| 96 | + temp_col = temp_col + (1 if (gate & 1) else 0) |
| 97 | + |
| 98 | + if temp_row < 0: |
| 99 | + temp_row = temp_row + row_len |
| 100 | + if temp_col < 0: |
| 101 | + temp_col = temp_col + col_len |
| 102 | + if temp_row >= row_len: |
| 103 | + temp_row = temp_row - row_len |
| 104 | + if temp_col >= col_len: |
| 105 | + temp_col = temp_col - col_len |
| 106 | + |
| 107 | + b1 = row * row_len + col |
| 108 | + b2 = temp_row * row_len + temp_col |
| 109 | + |
| 110 | + if (b1 >= width) or (b2 >= width): |
| 111 | + continue |
| 112 | + |
| 113 | + if d & 1: |
| 114 | + t = b1 |
| 115 | + b1 = b2 |
| 116 | + b2 = t |
| 117 | + |
| 118 | + g = random.choice(two_bit_gates) |
| 119 | + g(rcs, b1, b2) |
| 120 | + |
| 121 | + experiment = QrackSimulator(width, isTensorNetwork=False, isSparse=True, isOpenCL=False) |
| 122 | + if sdrp > 0: |
| 123 | + experiment.set_sdrp(sdrp) |
| 124 | + experiment.run_qiskit_circuit(rcs) |
| 125 | + experiment_counts = dict(Counter(experiment.measure_shots(all_bits, shots))) |
| 126 | + experiment_counts = sorted(experiment_counts.items(), key=operator.itemgetter(1)) |
| 127 | + |
| 128 | + quimb_rcs = quimb_circuit(rcs) |
| 129 | + n_pow = 1 << width |
| 130 | + u_u = 1 / n_pow |
| 131 | + idx = 0 |
| 132 | + ideal_probs = {} |
| 133 | + sum_probs = 0 |
| 134 | + for count_tuple in experiment_counts: |
| 135 | + key = count_tuple[0] |
| 136 | + prob = abs(quimb_rcs.amplitude(int_to_bitstring(key, width), backend="jax")) ** 2 |
| 137 | + if prob <= u_u: |
| 138 | + continue |
| 139 | + val = count_tuple[1] |
| 140 | + ideal_probs[key] = val |
| 141 | + sum_probs += val |
| 142 | + if len(ideal_probs) >= retained: |
| 143 | + break |
| 144 | + |
| 145 | + for key in ideal_probs.keys(): |
| 146 | + ideal_probs[key] = ideal_probs[key] / sum_probs |
| 147 | + |
| 148 | + rcs.save_statevector() |
| 149 | + control = AerSimulator(method="statevector") |
| 150 | + job = control.run(rcs) |
| 151 | + control_probs = Statevector(job.result().get_statevector()).probabilities() |
| 152 | + |
| 153 | + return calc_stats(control_probs, ideal_probs, shots, depth) |
| 154 | + |
| 155 | + |
| 156 | +def calc_stats(ideal_probs, exp_probs, shots, depth): |
| 157 | + # For QV, we compare probabilities of (ideal) "heavy outputs." |
| 158 | + # If the probability is above 2/3, the protocol certifies/passes the qubit width. |
| 159 | + n_pow = len(ideal_probs) |
| 160 | + n = int(round(math.log2(n_pow))) |
| 161 | + mean_guess = 1 / n_pow |
| 162 | + model = 1 / math.sqrt(n) |
| 163 | + threshold = statistics.median(ideal_probs) |
| 164 | + u_u = statistics.mean(ideal_probs) |
| 165 | + numer = 0 |
| 166 | + denom = 0 |
| 167 | + sum_hog_counts = 0 |
| 168 | + sqr_diff = 0 |
| 169 | + for i in range(n_pow): |
| 170 | + exp = (1 - model) * (exp_probs[i] if i in exp_probs else 0) + model * mean_guess |
| 171 | + ideal = ideal_probs[i] |
| 172 | + |
| 173 | + # XEB / EPLG |
| 174 | + denom += (ideal - u_u) ** 2 |
| 175 | + numer += (ideal - u_u) * (exp - u_u) |
| 176 | + |
| 177 | + # L2 norm |
| 178 | + sqr_diff += (ideal - exp) ** 2 |
| 179 | + |
| 180 | + # QV / HOG |
| 181 | + if ideal > threshold: |
| 182 | + sum_hog_counts += exp * shots |
| 183 | + |
| 184 | + hog_prob = sum_hog_counts / shots |
| 185 | + xeb = numer / denom |
| 186 | + # p-value of heavy output count, if method were actually 50/50 chance of guessing |
| 187 | + p_val = ( |
| 188 | + (1 - binom.cdf(sum_hog_counts - 1, shots, 1 / 2)) if sum_hog_counts > 0 else 1 |
| 189 | + ) |
| 190 | + rss = math.sqrt(sqr_diff) |
| 191 | + |
| 192 | + return { |
| 193 | + "qubits": n, |
| 194 | + "depth": depth, |
| 195 | + "xeb": float(xeb), |
| 196 | + "hog_prob": float(hog_prob), |
| 197 | + "l2_diff": float(rss), |
| 198 | + "p-value": float(p_val), |
| 199 | + } |
| 200 | + |
| 201 | + |
| 202 | +def main(): |
| 203 | + if len(sys.argv) < 3: |
| 204 | + raise RuntimeError( |
| 205 | + "Usage: python3 fc_qiskit_validation.py [width] [depth] [trials]" |
| 206 | + ) |
| 207 | + |
| 208 | + width = int(sys.argv[1]) |
| 209 | + depth = int(sys.argv[2]) |
| 210 | + sdrp = 0 |
| 211 | + if len(sys.argv) > 3: |
| 212 | + sdrp = float(sys.argv[3]) |
| 213 | + |
| 214 | + # Run the benchmarks |
| 215 | + print(bench_qrack(width, depth, sdrp)) |
| 216 | + |
| 217 | + return 0 |
| 218 | + |
| 219 | + |
| 220 | +if __name__ == "__main__": |
| 221 | + sys.exit(main()) |
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