Quantum Dataset Generators API

Quantum Dataset Generators

graphcalc.quantum.dataset_generators.generate_parameter_grid(*, state_specs: Sequence[Mapping[str, Any]], channel_specs: Sequence[Mapping[str, Any]] | None = None, subsystem_patterns: Sequence[Sequence[int]] | None = None, measurement_families: Sequence[str] | None = None, include_measurements: bool = True) → Dict[str, Any][source]

Generate a conjecturing-ready dataset package together with metadata.

Parameters:

  • state_specs (sequence of mappings) – Specifications describing base-state families.

  • channel_specs (sequence of mappings | None, default=None) – Specifications describing local channel families.

  • subsystem_patterns (sequence of sequences of int | None, default=None) – Target subsystem patterns for local channel application.

  • measurement_families (sequence of str | None, default=None) – Optional measurement families for probability summaries.

  • include_measurements (bool, default=True) – Whether to include measurement summary columns.

Returns:

Dictionary with keys:

  • "rows" : list of row dictionaries,

  • "column_definitions" : dictionary mapping columns to meanings,

  • "metadata" : summary information about the generated dataset.

Return type:

dict

graphcalc.quantum.dataset_generators.generate_quantum_state_dataset(*, state_specs: Sequence[Mapping[str, Any]], channel_specs: Sequence[Mapping[str, Any]] | None = None, subsystem_patterns: Sequence[Sequence[int]] | None = None, measurement_families: Sequence[str] | None = None, include_measurements: bool = True) → List[Dict[str, Any]][source]

Generate a conjecturing-oriented dataset of quantum-state snapshots.

Parameters:

  • state_specs (sequence of mappings) – Specifications describing base-state families to generate.

  • channel_specs (sequence of mappings | None, default=None) – Specifications describing local channel families. If omitted, only the base states are used.

  • subsystem_patterns (sequence of sequences of int | None, default=None) – Subsystem index patterns on which the selected local channels are applied. If omitted, the empty pattern is used, meaning no noise.

  • measurement_families (sequence of str | None, default=None) – Optional measurement families for probability summaries. Supported values currently include: "computational_basis", "pauli_x", "pauli_y", "pauli_z", and "bell_basis" when dimension permits.

  • include_measurements (bool, default=True) – Whether to include measurement summary columns.

Returns:

Dataset rows suitable for conversion to a pandas DataFrame.

Return type:

list of dict

Notes

Each row corresponds to:

  • one base state specification,

  • one local channel specification (or none),

  • one subsystem pattern,

  • one optional measurement family.

The resulting rows are intentionally denormalized so that each row is self-contained for downstream conjecturing code.

graphcalc.quantum.dataset_generators.large_quantum_snapshot() → Dict[str, Any][source]

Return a larger snapshot dataset for more substantial conjecturing runs.

Notes

This snapshot is still deliberately bounded to standard low-dimensional families, but it creates a much richer Cartesian-product sweep over states, local noise models, subsystem patterns, and measurements.

graphcalc.quantum.dataset_generators.medium_quantum_snapshot() → Dict[str, Any][source]

Return a medium-sized snapshot dataset for broader conjecturing sweeps.

Notes

This snapshot expands both the family diversity and the parameter grid, while remaining small enough to inspect interactively.

graphcalc.quantum.dataset_generators.quantum_dataset_column_definitions() → Dict[str, str][source]

Return a dictionary mapping dataset column names to mathematical meanings.

Notes

The returned dictionary is intended to describe the columns produced by generate_quantum_state_dataset. Some columns may be absent from a particular dataset row if the corresponding quantity is not applicable.

graphcalc.quantum.dataset_generators.small_quantum_snapshot() → Dict[str, Any][source]

Return a small snapshot dataset for quick experimentation.

Notes

This snapshot is intentionally compact and includes a small variety of state families, a few low-complexity noise settings, and basic measurement summaries.