qbraid.runtime.schemas.QuboSolveParams

class QuboSolveParams(**data)[source]

Parameters for solving a QUBO problem using NEC VA algorithm.

offset

Offset for the normalized weight information stored in the QUBO.

Type:: float

num_reads

VA sampling rate. Must be between 1 and 20. Default is 1.

Type:: Optional[int]

num_results

Number of VA annealing results. Returns only the optimal solution when 1 or None is specified. Default is 1.

Type:: Optional[int]

num_sweeps

Number of VA annealing sweeps. Must be between 1 and 100000. Default is 500.

Type:: Optional[int]

beta_range

VA beta value in (start, end, steps) format. Default is (10.0, 100.0, 200).

Type:: Optional[tuple[float, float, int]]

beta_list

Beta value array for each VA sweep.

Type:: Optional[list[float]]

dense

VA matrix mode. True for dense matrix mode, False for sparse matrix mode. Default is None.

Type:: Optional[bool]

vector_mode

Mode during VA annealing. Options are ‘speed’ for speed priority or ‘accuracy’ for accuracy priority. Default is ‘accuracy’.

Type:: Optional[str]

timeout

Job execution timeout in seconds. Standard range is between 1 and 7200. Default is 1800.

Type:: Optional[int]

ve_num

Number of VEs used in VA annealing. Must be between 1 and the number of VEs installed on each server.

Type:: Optional[int]

onehot

VA onehot constraint.

Type:: Optional[list[list[str]]]

fixed

VA fixed constraint.

Type:: Optional[Union[dict[str, int], list[list[Union[str, int]]]]

andzero

VA andzero constraint.

Type:: Optional[list[list[str]]]

orone

VA orone constraint.

Type:: Optional[list[list[str]]]

supplement

VA supplement constraint.

Type:: Optional[list[list[str]]]

maxone

VA maxone constraint.

Type:: Optional[list[list[Union[int, list[str]]]]

minmaxone

VA minmaxone constraint.

Type:: Optional[list[list[Union[int, list[str]]]]

init_spin

VA initial spin parameter.

Type:: Optional[Union[dict[str, int], list[list[Union[str, int]]]]

spin_list

VA spin list parameter.

Type:: Optional[list[str]]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

`__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`from_orm`(obj)
`json`(*[, include, exclude, by_alias, ...])
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
`model_dump`(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
`model_dump_json`(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(_BaseModel__context)	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
`model_validate_strings`(obj, *[, strict, context])	Validate the given object with string data against the Pydantic model.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`update_forward_refs`(**localns)
`validate`(value)
`validate_beta_list`(value)	Validate the beta_list value.
`validate_beta_range`(value)	Validate the beta_range value.
`validate_num_reads`(value)	Validate the num_reads value.
`validate_num_sweeps`(value)	Validate the num_sweeps value.
`validate_offset`(value)	Validate the offset value.
`validate_timeout`(value)	Validate the timeout value.
`validate_ve_num`(value)	Validate the ve_num value.
`validate_vector_mode`(value)	Validate the vector_mode value.

Attributes

`model_computed_fields`
`model_config`	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.
`offset`
`num_reads`
`num_results`
`num_sweeps`
`beta_range`
`beta_list`
`dense`
`vector_mode`
`timeout`
`ve_num`
`onehot`
`fixed`
`andzero`
`orone`
`supplement`
`maxone`
`minmaxone`
`init_spin`
`spin_list`