Configs

ClassicalConfig

Bases: Config

A ClassicalConfig instance defines the classical part of a SolverConfig.

ATTRIBUTE	DESCRIPTION
classical_solver_type	Classical solver type. Defaults to "cplex". TYPE: str | ClassicalSolverType
cplex_maxtime	CPLEX maximum runtime. Defaults to 600s. TYPE: float
cplex_log_path	CPLEX log path. Default to solver.log. TYPE: str
max_iter	Maximum number of iterations to perform for simulated annealing or tabu search. TYPE: int
max_bitstrings	Maximal number of bitstrings returned as solutions. TYPE: int
sa_initial_temp	Starting temperature (controls exploration). TYPE: float
sa_final_temp	Minimum temperature threshold for stopping. TYPE: float
sa_alpha	Cooling rate - should be slightly below 1 (e.g., 0.95–0.99). TYPE: float
tabu_x0	The initial binary solution tensor of shape (n,). TYPE: Tensor | None
tabu_tenure	Number of iterations a move (bit flip) remains tabu. TYPE: int
tabu_max_no_improve	Maximum number of consecutive iterations without improvement before termination. TYPE: int

Config

Bases: BaseModel, ABC

Pydantic class for configs.

EmbeddingConfig

Bases: Config

A EmbeddingConfig instance defines the embedding part of a SolverConfig.

ATTRIBUTE	DESCRIPTION
embedding_method	The type of embedding method used to place atoms on the register according to the QUBO problem. Defaults to EmbedderType.GREEDY. TYPE: str | EmbedderType | type[BaseEmbedder]
greedy_layout	Layout type for the greedy embedder method. Defaults to LayoutType.TRIANGULAR. TYPE: LayoutType | str
greedy_traps	The number of traps on the register. Defaults to DeviceType.ANALOG_DEVICE.value.min_layout_traps. TYPE: int
greedy_spacing	The minimum distance between atoms. Defaults to DeviceType.ANALOG_DEVICE.value.min_atom_distance. TYPE: float
greedy_density	The estimated density of the QUBO matrix. Defaults to None. TYPE: float
blade_steps_per_round	The number of steps for each layer of dimension for BLaDE. Defaults to 200. TYPE: int
blade_starting_positions	The starting parameters according to the specified dimensions. Defaults to None. TYPE: Tensor | None
blade_dimensions	A list of dimension degrees to explore one after the other (default is [5, 4, 3, 2, 2, 2]). TYPE: list[int]
draw_steps	Show generated graph at each step of the optimization. Defaults to False. TYPE: bool
animation_save_path	If provided, path to save animation. Defaults to None. TYPE: str | None

PulseShapingConfig

Bases: Config

A PulseShapingConfig instance defines the pulse shaping part of a SolverConfig.

ATTRIBUTE	DESCRIPTION
pulse_shaping_method	Pulse shaping method used. Defauts to PulseType.ADIABATIC. TYPE: str | PulseType | type[BasePulseShaper]
dmm	Whether to use a detuning map when applying pulse shaping or not. This gets added to the pulse sequence as a ConstantWaveform. Defaults to True, which applies DMM in pulse. TYPE: bool
re_execute_opt_pulse	Whether to re-run the optimal pulse sequence. Defaults to False. TYPE: bool
optimized_n_calls	Number of calls for the optimization process inside VQA. Defaults to 20. Note the optimizer accepts a minimal value of 12. TYPE: int
optimized_initial_omega_parameters	Default initial omega parameters for the pulse. Defaults to Omega = (5, 10, 5). TYPE: List[float]
optimized_initial_detuning_parameters	Default initial detuning parameters for the pulse. Defaults to delta = (-10, 0, 10). TYPE: List[float]
optimized_custom_qubo_cost	Apply a different qubo cost evaluation than the default QUBO evaluation defined in qubosolver/pipeline/pulse.py:OptimizedPulseShaper.compute_qubo_cost. Must be defined as: def optimized_custom_qubo_cost(bitstring: str, QUBO: torch.Tensor) -> float. Defaults to None, meaning we use the default QUBO evaluation. TYPE: Callable[[str, Tensor], float]
optimized_custom_objective_fn	For bayesian optimization, one can change the output of qubosolver/pipeline/pulse.py:OptimizedPulseShaper.run_simulation to optimize differently. Instead of using the best cost out of the samples, one can change the objective for an average, or any function out of the form cost_eval = optimized_custom_objective_fn(bitstrings, counts, probabilities, costs, best_cost, best_bitstring) Defaults to None, which means we optimize using the best cost out of the samples. TYPE: Callable[[list, list, list, list, float, str], float]
optimized_callback_objective	Apply a callback during bayesian optimization. Only accepts one input dictionary created during optimization d = {"x": x, "cost_eval": cost_eval} hence should be defined as: def callback_fn(d: dict) -> None: Defaults to None, which means no callback is applied. TYPE: Callable[..., None]

SolverConfig

Bases: Config

A SolverConfig instance defines how a QUBO problem should be solved. We specify whether to use a quantum or classical approach, which backend to run on, and additional execution parameters.

ATTRIBUTE	DESCRIPTION
config_name	The name of the current configuration. Defaults to ''. TYPE: str
use_quantum	Whether to solve using a quantum approach (True) or a classical approach (False). Defaults to False. TYPE: bool
backend	Which underlying backend configuration is used. Defaults to the default BackendConfig using BackendType.QUTIP. TYPE: BackendConfig
embedding	Embedding part configuration of the solver. TYPE: EmbeddingConfig
pulse_shaping	Pulse-shaping part configuration of the solver. TYPE: PulseShapingConfig
classical	Classical part configuration of the solver. TYPE: ClassicalConfig
num_shots	Number of samples. Defaults to 500. TYPE: int
do_postprocessing	Whether we apply post-processing (True) or not (False). TYPE: bool
do_preprocessing	Whether we apply pre-processing (True) or not (False) TYPE: bool

from_kwargs(**kwargs) classmethod

Create an instance based on entries of other configs.

Note that if any of the keywords ("backend_config", "embedding", "pulse_shaping", "classical") are present in kwargs, the values are taken directly.

RETURNS	DESCRIPTION
SolverConfig	An instance from values. TYPE: SolverConfig

Source code in qubosolver/config.py

@classmethod
def from_kwargs(cls, **kwargs: dict) -> SolverConfig:
    """Create an instance based on entries of other configs.

    Note that if any of the keywords
    ("backend_config", "embedding", "pulse_shaping", "classical")
    are present in kwargs, the values are taken directly.

    Returns:
        SolverConfig: An instance from values.
    """
    # Extract fields from pydantic BaseModel
    backend_config_fields = {k: v for k, v in kwargs.items() if k in BackendConfig.model_fields}
    embedding_fields = {k: v for k, v in kwargs.items() if k in EmbeddingConfig.model_fields}
    pulse_shaping_fields = {
        k: v for k, v in kwargs.items() if k in PulseShapingConfig.model_fields
    }
    classical_fields = {k: v for k, v in kwargs.items() if k in ClassicalConfig.model_fields}

    solver_fields = {
        k: v
        for k, v in kwargs.items()
        if k in cls.model_fields
        and k not in ("backend_config", "embedding", "pulse_shaping", "classical")
    }

    return cls(
        backend_config=(
            BackendConfig(**backend_config_fields)
            if "backend_config" not in kwargs
            else kwargs["backend_config"]
        ),
        embedding=(
            EmbeddingConfig(**embedding_fields)
            if "embedding" not in kwargs
            else kwargs["embedding"]
        ),
        pulse_shaping=(
            PulseShapingConfig(**pulse_shaping_fields)
            if "pulse_shaping" not in kwargs
            else kwargs["pulse_shaping"]
        ),
        classical=(
            ClassicalConfig(**classical_fields)
            if "classical" not in kwargs
            else kwargs["classical"]
        ),
        **solver_fields,
    )

print_specs()

Print specs.

Source code in qubosolver/config.py

def print_specs(self) -> None:
    """Print specs."""
    print(self.specs())

specs()

Return the specs of the SolverConfig, that is all attributes.

RETURNS	DESCRIPTION
dict	Dictionary of specs key-values. TYPE: str

Source code in qubosolver/config.py

def specs(self) -> str:
    """Return the specs of the `SolverConfig`, that is all attributes.

    Returns:
        dict: Dictionary of specs key-values.
    """
    return "\n".join(
        f"{k}: ''" if v == "" else f"{k}: {v}" for k, v in self.model_dump().items()
    )