qek.data.processed_data

source module qek.data.processed_data

Loading, saving, manipulation or processed data.

Classes

• ProcessedData — Data on a single graph obtained from the Quantum Device.

Functions

• dist_excitation — Calculates the distribution of excitation energies from a dictionary of bitstrings to their respective counts.

• save_dataset — Saves a dataset to a JSON file.

• load_dataset — Loads a dataset from a JSON file.

source class ProcessedData(sequence: pl.Sequence, state_dict: dict[str, int | np.int64], target: int | None)

Data on a single graph obtained from the Quantum Device.

Attributes

• register : pl.Register — The geometry of atoms on the Quantum Device, obtained by compiling the graph for execution on the Quantum Device.

• pulse : pl.Pulse — The laser pulse, obtained by compiling the graph for execution on the Quantum Device.

• state_dict : Final[dict[str, int]] — A dictionary {bitstring: number of instances} for this graph.

• target : Final[int | None] — If specified, the machine-learning target, as a value 0 or 1.

Methods

• custom

• save_to_file

• dist_excitation — Return the distribution of excitations for this graph.

• draw_pulse — Draw the pulse on screen

• draw_register — Draw the register on screen

• draw_excitation — Draw an histogram for the excitation level on screen

source classmethod ProcessedData.custom(register: targets.Register, pulse: targets.Pulse, device: pl.devices.Device, state_dict: dict[str, int | np.int64], target: int | None) → ProcessedData

source method ProcessedData.save_to_file(file_path: str) → None

source method ProcessedData.dist_excitation(size: int | None = None) → np.ndarray

Return the distribution of excitations for this graph.

Parameters

• size : int | None — If specified, truncate or pad the array to this size.

source property ProcessedData.pulse: pl.Pulse

The laser pulse used to process this data.

source property ProcessedData.register: pl.Register

The register to which the graph was compiled.

source method ProcessedData.draw_pulse() → None

Draw the pulse on screen

source method ProcessedData.draw_register() → None

Draw the register on screen

source method ProcessedData.draw_excitation() → None

Draw an histogram for the excitation level on screen

source dist_excitation(state_dict: dict[str, int], size: int | None = None) → np.ndarray

Calculates the distribution of excitation energies from a dictionary of bitstrings to their respective counts.

Parameters

• size : int | None — If specified, only keep size energy distributions in the output. Otherwise, keep all values.

Returns

• np.ndarray — A histogram of excitation energies. - index: an excitation level (i.e. a number of 1 bits in a bitstring) - value: normalized count of samples with this excitation level.

source save_dataset(dataset: list[ProcessedData], file_path: str) → None

Saves a dataset to a JSON file.

Parameters

• dataset : list[ProcessedData] — The dataset to be saved, containing RegisterData instances.

• file_path : str — The path where the dataset will be saved as a JSON file.

Note

The data is stored in a format suitable for loading with load_dataset.

Returns

• None — None

source load_dataset(file_path: str) → list[ProcessedData]

Loads a dataset from a JSON file.

Parameters

• file_path : str — The path to the JSON file containing the dataset.

Note

The data is loaded in the format that was used when saving with save_dataset.

Returns

• list[ProcessedData] — A list of ProcessedData instances, corresponding to the data stored in the JSON file.