uniqc.task.normalizers module#

Platform-specific result normalizers.

This module provides functions to convert platform-specific result formats into the unified UnifiedResult format. Each platform (OriginQ, Quafu, IBM) has its own normalizer that handles the unique output format of that platform.

The normalizers are used by the adapter classes to ensure consistent result handling across all platforms.

Usage:

from uniqc.task.normalizers import normalize_quafu
from uniqc.task.result_types import UnifiedResult

# Convert Quafu result to unified format
unified = normalize_quafu(quafu_result, task_id="abc123")
uniqc.task.normalizers.normalize_dummy(probs_list, task_id, shots=1000)[source]#

Normalize local simulator probability output.

The local OriginIR simulator returns a list of probabilities indexed by computational basis state (little-endian).

Parameters:

  • probs_list (list[float]) – List of probabilities indexed by basis state.

  • task_id (str) – Task identifier.

  • shots (int) – Number of shots.

Returns:

UnifiedResult with probabilities converted to bitstrings.

Return type:

UnifiedResult

Example

>>> probs = [0.5, 0.0, 0.0, 0.5]  # |00> and |11> each 50%
>>> result = normalize_dummy(probs, "task-3")
>>> print(result.probabilities)
{'00': 0.5, '11': 0.5}
uniqc.task.normalizers.normalize_ibm(result_obj, task_id)[source]#

Normalize IBM Quantum (Qiskit) Result format.

IBM returns a Qiskit Result object with:

  • get_counts(): Returns dict or list of dicts for measurement counts

  • to_dict(): Returns full result as dict with metadata

Parameters:

  • result_obj (Any) – Qiskit Result object.

  • task_id (str) – Task identifier (Qiskit job ID).

Returns:

UnifiedResult with counts and probabilities.

Return type:

UnifiedResult

Note

For batch jobs, this normalizes the first circuit result only. Use result_obj.get_counts() directly for batch results.

Example

>>> # result_obj is a qiskit Result
>>> unified = normalize_ibm(result_obj, "job-123")
>>> print(unified.counts)
{'0x0': 512, '0x3': 488}
uniqc.task.normalizers.normalize_originq(raw, task_id, shots=1000, n_qubits=None)[source]#

Normalize OriginQ Cloud result format.

OriginQ returns results in the format:

{‘key’: [‘0x0’, ‘0x1’, …], ‘value’: [0.5, 0.3, …]}

where keys are hexadecimal bitstrings and values are probabilities.

Parameters:

  • raw (Dict[str, Any]) – Raw result dict from OriginQ Cloud API.

  • task_id (str) – Task identifier.

  • shots (int) – Number of shots (default 1000).

  • n_qubits (int | None) – Number of qubits. If None, inferred from keys.

Returns:

UnifiedResult with normalized probabilities and counts.

Return type:

UnifiedResult

Example

>>> raw = {'key': ['0x0', '0x3'], 'value': [0.5, 0.5]}
>>> result = normalize_originq(raw, "task-1", n_qubits=2)
>>> print(result.probabilities)
{'00': 0.5, '11': 0.5}
uniqc.task.normalizers.normalize_quafu(result_obj, task_id, backend_name=None)[source]#

Normalize Quafu ExecResult format.

Quafu returns an ExecResult object with attributes:

  • counts: Dict[str, int] measurement counts

  • probabilities: Dict[str, float] measurement probabilities

  • task_status: Status string

Parameters:

  • result_obj (Any) – Quafu ExecResult object.

  • task_id (str) – Task identifier.

  • backend_name (str | None) – Optional backend name override.

Returns:

UnifiedResult with counts and probabilities.

Return type:

UnifiedResult

Example

>>> # result_obj is a quafu ExecResult
>>> unified = normalize_quafu(result_obj, "task-2")
>>> print(unified.counts)
{'00': 512, '11': 488}