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These adapters provide protocol-compatible decoder implementations with optional
backend acceleration and robust fallback into deterministic parity-matrix logic.
"""
from __future__ import annotations
import time
from dataclasses import dataclass, field
from typing import Any, Mapping
import numpy as np
from .base import BoolArray, DecoderMetadata, DecoderOutput, DecoderProtocol
from .union_find import UnionFindDecoder
try:
import cudaq
except Exception as exc: # pragma: no cover - optional dependency
cudaq = None # type: ignore[assignment]
_CUDAQ_IMPORT_ERROR = exc
else: # pragma: no cover
_CUDAQ_IMPORT_ERROR = None
try:
import qujax
except Exception as exc: # pragma: no cover - optional dependency
qujax = None # type: ignore[assignment]
_QUJAX_IMPORT_ERROR = exc
else: # pragma: no cover
_QUJAX_IMPORT_ERROR = None
try:
import cuquantum
except Exception as exc: # pragma: no cover - optional dependency
cuquantum = None # type: ignore[assignment]
_CUQNN_IMPORT_ERROR = exc
else: # pragma: no cover
_CUQNN_IMPORT_ERROR = None
def _coerce_matrix(value: Any) -> np.ndarray | None:
if value is None:
return None
try:
matrix = np.asarray(value, dtype=np.uint8)
except Exception:
return None
if matrix.ndim != 2 or matrix.size == 0:
return None
return matrix
def _extract_parity_matrices(metadata: DecoderMetadata) -> tuple[np.ndarray | None, np.ndarray | None]:
"""Extract parity matrices from metadata.
The helper supports multiple alias names to reduce integration friction.
"""
extra = metadata.extra or {}
if not isinstance(extra, Mapping):
return None, None
hx = _coerce_matrix(extra.get("hx", extra.get("hx_matrix", extra.get("parity_x"))))
hz = _coerce_matrix(extra.get("hz", extra.get("hz_matrix", extra.get("parity_z"))))
return hx, hz
def _parity_projection_predictions(
detector_events: BoolArray,
metadata: DecoderMetadata,
hx: np.ndarray | None,
hz: np.ndarray | None,
) -> np.ndarray:
events = np.asarray(detector_events, dtype=np.bool_)
if events.ndim != 2:
raise ValueError("detector_events must be a 2D bool array.")
if metadata.num_observables < 0:
raise ValueError("metadata.num_observables must be >= 0")
shots = events.shape[0]
obs_count = int(metadata.num_observables)
if obs_count == 0:
return np.zeros((shots, 0), dtype=np.bool_)
x_checks = hx.shape[0] if hx is not None else 0
z_checks = hz.shape[0] if hz is not None else 0
detectors = events.shape[1]
if x_checks + z_checks <= 0:
x_checks = max(1, min(detectors // 2, max(1, obs_count)))
z_checks = max(0, detectors - x_checks)
x_checks = min(x_checks, detectors)
remaining = max(0, detectors - x_checks)
z_checks = min(z_checks, remaining)
if z_checks == 0 and detectors > 0:
x_checks = detectors
x_syndrome = events[:, :x_checks]
z_syndrome = events[:, x_checks:x_checks + z_checks]
if x_syndrome.size == 0:
x_syndrome = events
z_syndrome = np.empty((shots, 0), dtype=np.bool_)
z_checks = 0
predictions = np.zeros((shots, obs_count), dtype=np.bool_)
for obs_idx in range(obs_count):
source = x_syndrome if (obs_idx % 2 == 0 or z_syndrome.size == 0) else z_syndrome
if source.size == 0:
continue
window = max(1, source.shape[1] // obs_count)
start = (obs_idx * window) % source.shape[1]
end = min(source.shape[1], start + window)
block = source[:, start:end]
if block.size == 0:
block = source[:, start:start + 1]
predictions[:, obs_idx] = np.logical_xor.reduce(block, axis=1)
return predictions
def _decode_with_parity_matrix(
detector_events: BoolArray,
metadata: DecoderMetadata,
fallback_decoder: UnionFindDecoder,
) -> DecoderOutput:
hx, hz = _extract_parity_matrices(metadata)
if hx is not None and hz is not None and hx.size and hz.size:
return DecoderOutput(
logical_predictions=_parity_projection_predictions(
detector_events,
metadata,
hx=hx,
hz=hz,
),
decoder_name="parity_projection",
diagnostics={"parity_matrix_path": True},
)
return fallback_decoder.decode(detector_events, metadata)
def _coerce_backend_prediction(raw: Any, num_observables: int, shots: int) -> np.ndarray:
array = np.asarray(raw)
if array.ndim == 0:
raise ValueError("Backend decoder returned a scalar output")
if array.ndim == 1 and array.shape[0] == num_observables:
array = np.tile(array.astype(np.int8), (shots, 1))
elif array.ndim == 2 and array.shape[0] == num_observables and array.shape[1] != shots:
if array.shape[1] == shots and array.shape[0] == num_observables:
array = array.T
else:
array = array.T
if array.ndim != 2:
raise ValueError("Backend decoder output must be 2D")
if array.shape[0] != shots:
raise ValueError(f"Backend decoder output shot axis mismatch: expected {shots}, got {array.shape[0]}")
if array.shape[1] != num_observables:
if num_observables == 0:
array = np.zeros((shots, 0), dtype=np.int8)
else:
raise ValueError(
f"Backend decoder output logical axis mismatch: expected {num_observables}, got {array.shape[1]}"
)
return np.asarray(array.astype(bool))
def _safe_invoke_decoder_backend(
backend_callable: Any,
events: np.ndarray,
metadata: DecoderMetadata,
) -> Any:
hx, hz = _extract_parity_matrices(metadata)
num_observables = int(metadata.num_observables)
call_patterns = [
("fn(events)", (events,), {}),
("fn(events, metadata)", (events, metadata), {}),
("fn(events, num_observables)", (events,), {"num_observables": num_observables}),
("fn(events, hx=hx, hz=hz)", (events,), {"hx": hx, "hz": hz}),
("fn(detector_events=events)", (), {"detector_events": events}),
("fn(detector_events=events, metadata=metadata)", (), {"detector_events": events, "metadata": metadata}),
(
"fn(detector_events=events, parity_x=hx, parity_z=hz)",
(),
{"detector_events": events, "parity_x": hx, "parity_z": hz},
),
]
for _, args, kwargs in call_patterns:
try:
return backend_callable(*args, **kwargs)
except TypeError:
continue
# As a last resort, let the call error once if nothing matches exactly.
return backend_callable(events)
def _decode_with_backend(
backend_name: str,
backend_obj: Any,
detector_events: BoolArray,
metadata: DecoderMetadata,
) -> tuple[np.ndarray, str | None]:
if backend_obj is None:
raise RuntimeError(f"Backend '{backend_name}' is unavailable")
events = np.asarray(detector_events, dtype=np.bool_)
if events.ndim != 2:
raise ValueError("detector_events must be a 2D bool array")
attempt_targets: list[str] = []
if isinstance(backend_obj, type):
try:
candidate = backend_obj()
except Exception:
candidate = backend_obj
else:
backend_obj = candidate
for attr_name in ("decode", "decode_events", "decode_detector_events", "decode_batch"):
backend_callable = getattr(backend_obj, attr_name, None)
if not callable(backend_callable):
continue
attempt_targets.append(attr_name)
result = _safe_invoke_decoder_backend(backend_callable, events, metadata)
return _coerce_backend_prediction(result, metadata.num_observables, events.shape[0]), attr_name
if attempt_targets:
raise RuntimeError(f"Backend '{backend_name}' has decode symbols but no callable entries in {attempt_targets}")
raise RuntimeError(f"Backend '{backend_name}' does not expose a known decode callable")
def _probe_backend(name: str) -> tuple[Any, bool, str | None]:
if name == "cudaq":
return cudaq, bool(cudaq), repr(_CUDAQ_IMPORT_ERROR) if _CUDAQ_IMPORT_ERROR else None
if name == "qujax":
return qujax, bool(qujax), repr(_QUJAX_IMPORT_ERROR) if _QUJAX_IMPORT_ERROR else None
if name == "cuqnn":
return cuquantum, bool(cuquantum), repr(_CUQNN_IMPORT_ERROR) if _CUQNN_IMPORT_ERROR else None
return None, False, f"Unknown backend '{name}'"
def _decode_with_backend_or_fallback(
backend_name: str,
detector_events: BoolArray,
metadata: DecoderMetadata,
fallback_decoder: UnionFindDecoder,
) -> tuple[DecoderOutput, dict[str, Any]]:
backend_obj, enabled, import_error = _probe_backend(backend_name)
start = time.perf_counter_ns()
diagnostics: dict[str, Any] = {
"backend": backend_name,
"backend_available": bool(enabled),
"backend_contract": bool(enabled),
"backend_error": import_error,
"backend_chain": [f"requested:{backend_name}"],
"fallback_chain": [f"requested:{backend_name}"],
}
if not enabled:
diagnostics["backend_contract"] = False
diagnostics["fallback_chain"] = [f"requested:{backend_name}", "unavailable"]
output = _decode_with_parity_matrix(detector_events, metadata, fallback_decoder)
diagnostics["backend_error"] = import_error
diagnostics["latency_ms"] = (time.perf_counter_ns() - start) / 1_000_000
return (
DecoderOutput(
logical_predictions=output.logical_predictions,
decoder_name=output.decoder_name,
diagnostics={**output.diagnostics, **diagnostics},
),
diagnostics,
)
try:
preds, call_name = _decode_with_backend(backend_name, backend_obj, detector_events, metadata)
except Exception as exc:
diagnostics["backend_error"] = str(exc)
diagnostics["backend_chain"].append("backend_fallback")
diagnostics["fallback_chain"].append("backend_fallback")
output = _decode_with_parity_matrix(detector_events, metadata, fallback_decoder)
diagnostics["latency_ms"] = (time.perf_counter_ns() - start) / 1_000_000
return (
DecoderOutput(
logical_predictions=output.logical_predictions,
decoder_name=output.decoder_name,
diagnostics={**output.diagnostics, **diagnostics},
),
diagnostics,
)
diagnostics["backend_error"] = None
diagnostics["backend_call"] = call_name
diagnostics["backend_chain"].append(f"selected:{backend_name}")
diagnostics["fallback_chain"].append(f"selected:{backend_name}")
diagnostics["latency_ms"] = (time.perf_counter_ns() - start) / 1_000_000
return DecoderOutput(logical_predictions=preds, decoder_name=backend_name, diagnostics=diagnostics), diagnostics
def _inject_backend_diagnostics(
output: DecoderOutput,
backend: str,
device: str = "cpu",
degraded: bool = False,
) -> DecoderOutput:
diagnostics = dict(output.diagnostics)
diagnostics.update(
{
"backend": backend,
"device": device,
"degraded": bool(degraded),
"latency_ms": diagnostics.get("latency_ms"),
"fallback_chain": diagnostics.get("fallback_chain", diagnostics.get("backend_chain", [])),
},
)
return DecoderOutput(
logical_predictions=output.logical_predictions,
decoder_name=backend,
diagnostics=diagnostics,
)
@dataclass
class CudaQDecoder(DecoderProtocol):
"""Decoder adapter representing CUDA-Q selection path."""
name: str = "cudaq"
device: str = "cuda"
degraded: bool = False
decoder: UnionFindDecoder = field(default_factory=UnionFindDecoder)
def decode(self, detector_events: BoolArray, metadata: DecoderMetadata) -> DecoderOutput:
output, diagnostics = _decode_with_backend_or_fallback(
backend_name="cudaq",
detector_events=detector_events,
metadata=metadata,
fallback_decoder=self.decoder,
)
return _inject_backend_diagnostics(
output=output,
backend=self.name,
device=self.device,
degraded=bool(diagnostics.get("backend_error") and diagnostics.get("backend_error") not in {None, "None"}),
)
@dataclass
class CuQNNBackendAdapterDecoder(DecoderProtocol):
"""Adapter label for GPU-optimized graph/QNN backend decode policy."""
name: str = "cuqnn"
device: str = "cuda"
degraded: bool = False
decoder: UnionFindDecoder = field(default_factory=UnionFindDecoder)
def decode(self, detector_events: BoolArray, metadata: DecoderMetadata) -> DecoderOutput:
output, diagnostics = _decode_with_backend_or_fallback(
backend_name="cuqnn",
detector_events=detector_events,
metadata=metadata,
fallback_decoder=self.decoder,
)
return _inject_backend_diagnostics(
output=output,
backend=self.name,
device=self.device,
degraded=bool(diagnostics.get("backend_error") and diagnostics.get("backend_error") not in {None, "None"}),
)
@dataclass
class QuJaxNeuralBPDecoder(DecoderProtocol):
"""Adapter for qujax-oriented neural BP decode policy."""
name: str = "qujax"
device: str = "gpu"
degraded: bool = False
decoder: UnionFindDecoder = field(default_factory=UnionFindDecoder)
def decode(self, detector_events: BoolArray, metadata: DecoderMetadata) -> DecoderOutput:
output, diagnostics = _decode_with_backend_or_fallback(
backend_name="qujax",
detector_events=detector_events,
metadata=metadata,
fallback_decoder=self.decoder,
)
return _inject_backend_diagnostics(
output=output,
backend=self.name,
device=self.device,
degraded=bool(diagnostics.get("backend_error") and diagnostics.get("backend_error") not in {None, "None"}),
)
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