File size: 3,591 Bytes
b75888f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | """Learned calibrated ensemble of matching and constrained logical-coset matching."""
from pathlib import Path
import json
import numpy as np
import pymatching
import stim
from scipy.special import expit
def match_features(dem, packed, rounds):
dets = np.unpackbits(packed, axis=1, count=dem.num_detectors, bitorder='little')
m = pymatching.Matching.from_detector_error_model(dem, enable_correlations=True)
plain, wp = m.decode_batch(dets, return_weights=True)
corr, wc = m.decode_batch(dets, return_weights=True, enable_correlations=True)
# In these circuits the logical path is on a boundary. Converting L0 into
# one extra syndrome vertex enforces each logical coset separately.
cd = stim.DetectorErrorModel()
for instruction in dem.flattened():
if instruction.type != 'error':
continue
targets = [stim.target_relative_detector_id(dem.num_detectors)
if t.is_logical_observable_id() else t
for t in instruction.targets_copy()]
cd.append('error', instruction.args_copy(), targets)
cm = pymatching.Matching.from_detector_error_model(cd)
augmented = np.pad(dets, ((0, 0), (0, 1)))
_, w0 = cm.decode_batch(augmented, return_weights=True)
augmented[:, -1] = 1
_, w1 = cm.decode_batch(augmented, return_weights=True)
gap = np.clip(w0 - w1, -20, 20)
s0, s1 = plain[:, 0].astype(np.float64)*2-1, corr[:, 0].astype(np.float64)*2-1
density = dets.mean(axis=1)
r = np.log1p(rounds)/np.log(26)
return np.stack([np.ones(len(dets)), gap, s0, s1, s1*abs(gap),
gap*density, s1*density, gap*r, s1*r,
s0*density, s0*r, s0*s1], axis=1).astype(np.float32)
def predict_features(x, state):
return expit((x/state['scale']) @ state['coef']).astype(np.float32)
def infer_condition(task):
package, inputs, output, row, config = task
package, inputs, output = map(Path, (package, inputs, output))
# ID values are used only to echo the input alignment.
with np.load(inputs/row['arrays'], allow_pickle=False) as a:
packed, ids = a['detectors'], a['sample_ids']
record = config['conditions'][row['name']]
dem = stim.DetectorErrorModel.from_file(package/record['dem'])
with np.load(package/config['groups'][row['group']], allow_pickle=False) as a:
state = dict(a)
probabilities = np.empty(len(ids), dtype=np.float32)
for start in range(0, len(ids), 8192):
end = min(start+8192, len(ids))
x = match_features(dem, packed[start:end], row['rounds'])
probabilities[start:end] = predict_features(x, state)
np.savez(output/f"{row['name']}.npz", sample_ids=ids, probabilities=probabilities)
return row['name']
def main():
import sys, os
from concurrent.futures import ProcessPoolExecutor
package = Path(__file__).resolve().parent
inputs, output = map(Path, sys.argv[1:3])
manifest = json.loads((inputs/'manifest.json').read_text())
if manifest['schema'] != 'surface-code-decoding-input/v1':
raise ValueError('Unsupported input schema')
config = json.loads((package/'model_config.json').read_text())
output.mkdir(exist_ok=True, parents=True)
tasks = [(str(package), str(inputs), str(output), row, config) for row in manifest['conditions']]
workers = min(int(os.environ.get('QEC_WORKERS', '16')), len(tasks))
with ProcessPoolExecutor(max_workers=workers) as pool:
for name in pool.map(infer_condition, tasks):
print(name, flush=True)
if __name__ == '__main__':
main()
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