File size: 2,833 Bytes
32d1b4a | 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 | from pathlib import Path
import json,sys
import numpy as np
import stim
sys.path.insert(0,'/workspace/research/exact_d3')
from exact import make_plan,parse_errors
def load_all(max_ops=3072):
root=Path('/workspace/data')
rows=[r for r in json.loads((root/'train/manifest.json').read_text())['conditions'] if r['distance']==3]
groups=sorted({r['group'] for r in rows});result=[]
for row in rows:
dem=stim.DetectorErrorModel.from_file(root/'train'/row['noise']);plan=make_plan(dem)
assert int(plan['width'])<=10
count=len(plan['masks']);assert count<=max_ops,(row['name'],count)
coords=dem.get_detector_coordinates()
circuit=stim.Circuit.from_file(root/'train'/row['circuit'])
qubit_coords=circuit.get_final_qubit_coordinates()
first_measurement=next(ins for ins in circuit.flattened() if ins.name in ['M','MX','MR','MRX'])
spaces=sorted(tuple(qubit_coords[t.value][:2]) for t in first_measurement.targets_copy())
assert len(spaces)==8
idx={(int(c[2]),tuple(c[:2])):d for d,c in coords.items()}
errors=parse_errors(dem)
context=np.full((max_ops,40),200,np.int32)
static=np.zeros((max_ops,57),np.float32)
for op,source in enumerate(plan['source_indices']):
if source<0:continue
ds,logical,p=errors[source]
t=min(int(coords[d][2]) for d in ds) if ds else 0
for dt in range(-2,3):
for s,spatial in enumerate(spaces):context[op,(dt+2)*8+s]=idx.get((t+dt,spatial),200)
for d in ds:
c=coords[d];static[op,(int(c[2])-t+2)*8+spaces.index(tuple(c[:2]))]=1
static[op,40]=np.log(max(p,1e-7)/(1-max(p,1e-7)))/8
static[op,41]=logical;static[op,42]=len(ds)/4
static[op,43]=t/25;static[op,44]=row['rounds']/25
static[op,45]=t==0;static[op,46]=t>=row['rounds']-1
static[op,47+groups.index(row['group'])]=1
static[op,55]=row['basis']=='X';static[op,56]=1
masks=np.pad(plan['masks'],(0,max_ops-count))
measured=np.pad(plan['measured'],(0,max_ops-count),constant_values=-1)
p=np.pad(plan['probabilities'],(0,max_ops-count))
valid=((p>0)&(measured<0)).astype(np.float32)
logits=np.log(np.clip(p,1e-7,1-1e-7)/np.clip(1-p,1e-7,1-1e-7)).astype(np.float32)
item={'row':row,'context':context,'static':static,'masks':masks,'measured':measured,'base_logits':logits,'valid':valid}
for split in ['train','validation']:
a=np.load(root/split/row['arrays']);ds=np.unpackbits(a['detectors'],axis=1,count=row['num_detectors'],bitorder='little')
item[split+'_dets']=np.pad(ds,((0,0),(0,201-ds.shape[1])))
item[split+'_labels']=a['labels']
result.append(item)
return result
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