Index

create_model

create_model(
    entry_point: Union[QFunc, GenerativeQFunc],
    constraints: Optional[Constraints] = None,
    execution_preferences: Optional[
        ExecutionPreferences
    ] = None,
    preferences: Optional[Preferences] = None,
    classical_execution_function: Optional[CFunc] = None,
    out_file: Optional[str] = None,
) -> SerializedModel

Create a serialized model from a given Qmod entry function and additional parameters.

Parameters:

Name	Type	Description	Default
`entry_point`	`Union[QFunc, GenerativeQFunc]`	The entry point function for the model, which must be a QFunc named 'main'.	required
`constraints`	`Optional[Constraints]`	Constraints for the synthesis of the model. See Constraints (Optional).	`None`
`execution_preferences`	`Optional[ExecutionPreferences]`	Preferences for the execution of the model. See ExecutionPreferences (Optional).	`None`
`preferences`	`Optional[Preferences]`	Preferences for the synthesis of the model. See Preferences (Optional).	`None`
`classical_execution_function`	`Optional[CFunc]`	A function for the classical execution logic, which must be a CFunc (Optional).	`None`
`out_file`	`Optional[str]`	File path to write the Qmod model in native Qmod representation to (Optional).	`None`

Returns:

Name	Type	Description
`SerializedModel`	`SerializedModel`	A serialized model.

Raises:

Type	Description
`ClassiqError`	If the entry point function is not named 'main'.

synthesize

synthesize(
    serialized_model: SerializedModel,
    auto_show: bool = False,
) -> SerializedQuantumProgram

Synthesize a model with the Classiq engine to receive a quantum program. More details

Parameters:

Name	Type	Description	Default
`serialized_model`	`SerializedModel`	A model object serialized as a string.	required
`auto_show`	`bool`	boolean. whether to call `show` on the output	`False`

Returns:

Name	Type	Description
`SerializedQuantumProgram`	`SerializedQuantumProgram`	Quantum program serialized as a string. (See: QuantumProgram)

show

show(
    quantum_program: SerializedQuantumProgram,
    display_url: bool = True,
) -> None

Displays the interactive representation of the quantum program in the Classiq IDE.

Parameters:

Name	Type	Description	Default
`quantum_program`	`SerializedQuantumProgram`	The serialized quantum program to be displayed.	required
`display_url`	`bool`	Whether to print the url	`True`

Links

Visualization tool

execute

execute(
    quantum_program: SerializedQuantumProgram,
) -> ExecutionJob

Execute a quantum program. The preferences for execution are set on the quantum program using the method set_execution_preferences.

Parameters:

Name	Type	Description	Default
`quantum_program`	`SerializedQuantumProgram`	The quantum program to execute. This is the result of the synthesize method.	required

Returns:

Name	Type	Description
`ExecutionJob`	`ExecutionJob`	The result of the execution.

For examples please see Execution Documentation

ExecutionSession

ExecutionSession(
    quantum_program: Program,
    execution_preferences: Optional[
        ExecutionPreferences
    ] = None,
)

A session for executing a quantum program. ExecutionSession allows to execute the quantum program with different parameters and operations without the need to re-synthesize the model.

Attributes:

Name	Type	Description
`quantum_program`	`Union[SerializedQuantumProgram, QuantumProgram]`	The quantum program to execute.
`execution_preferences`	`Optional[ExecutionPreferences]`	Execution preferences for the Quantum Program.

Methods:

Name	Description
`batch_estimate`	Estimates the expectation value of the given Hamiltonian multiple times using the quantum program, with the given parameters for each iteration. The number of estimations is determined by the length of the parameters list.
`batch_sample`	Samples the quantum program multiple times with the given parameters for each iteration. The number of samples is determined by the length of the parameters list.
`estimate`	Estimates the expectation value of the given Hamiltonian using the quantum program.
`estimate_cost`	Estimates circuit cost using a classical cost function.
`sample`	Samples the quantum program with the given parameters, if any.
`submit_batch_estimate`	Initiates an execution job with the `batch_estimate` primitive.
`submit_batch_sample`	Initiates an execution job with the `batch_sample` primitive.
`submit_estimate`	Initiates an execution job with the `estimate` primitive.
`submit_sample`	Initiates an execution job with the `sample` primitive.
`update_execution_preferences`	Update the execution preferences for the session.

Attributes:

Name	Type	Description
`program`	`QuantumProgram`
`qprog`	`SerializedQuantumProgram`	Returns:

program

program: QuantumProgram = _deserialize_program(
    quantum_program
)

qprog

qprog: SerializedQuantumProgram

Returns:

Name	Type	Description
`SerializedQuantumProgram`	`SerializedQuantumProgram`	The serialized quantum program (str). See `QuantumProgram`.

batch_estimate

batch_estimate(
    hamiltonian: Hamiltonian,
    parameters: list[ExecutionParams],
) -> list[EstimationResult]

Estimates the expectation value of the given Hamiltonian multiple times using the quantum program, with the given parameters for each iteration. The number of estimations is determined by the length of the parameters list.

Parameters:

Name	Type	Description	Default
`hamiltonian`	`Hamiltonian`	The Hamiltonian to estimate the expectation value of.	required
`parameters`	`list[ExecutionParams]`	A list of the parameters for each iteration. Each item is a dictionary where each key should be the name of a parameter in the quantum program (parameters of the main function), and the value should be the value to set for that parameter.	required

Returns:

Type	Description
`list[EstimationResult]`	List[EstimationResult]: The results of all the estimation iterations.

batch_sample

batch_sample(
    parameters: list[ExecutionParams],
) -> list[ExecutionDetails]

Samples the quantum program multiple times with the given parameters for each iteration. The number of samples is determined by the length of the parameters list.

Parameters:

Name	Type	Description	Default
`parameters`	`list[ExecutionParams]`	A list of the parameters for each iteration. Each item is a dictionary where each key should be the name of a parameter in the quantum program (parameters of the main function), and the value should be the value to set for that parameter.	required

Returns:

Type	Description
`list[ExecutionDetails]`	List[ExecutionDetails]: The results of all the sampling iterations.

estimate

estimate(
    hamiltonian: Hamiltonian,
    parameters: Optional[ExecutionParams] = None,
) -> EstimationResult

Estimates the expectation value of the given Hamiltonian using the quantum program.

Parameters:

Name	Type	Description	Default
`hamiltonian`	`Hamiltonian`	The Hamiltonian to estimate the expectation value of.	required
`parameters`	`Optional[ExecutionParams]`	The values to set for the parameters of the quantum program when estimating. Each key should be the name of a parameter in the quantum program (parameters of the main function), and the value should be the value to set for that parameter.	`None`

Returns:

Name	Type	Description
`EstimationResult`	`EstimationResult`	The result of the estimation.

estimate_cost

estimate_cost(
    cost_func: Callable[[ParsedState], float],
    parameters: Optional[ExecutionParams] = None,
    quantile: float = 1.0,
) -> float

Estimates circuit cost using a classical cost function.

Parameters:

Name	Type	Description	Default
`cost_func`	`Callable[[ParsedState], float]`	classical circuit sample cost function	required
`parameters`	`Optional[ExecutionParams]`	execution parameters sent to 'sample'	`None`
`quantile`	`float`	drop cost values outside the specified quantile	`1.0`

Returns:

Type	Description
`float`	cost estimation

sample

sample(
    parameters: Optional[ExecutionParams] = None,
) -> ExecutionDetails

Samples the quantum program with the given parameters, if any.

Parameters:

Name	Type	Description	Default
`parameters`	`Optional[ExecutionParams]`	The values to set for the parameters of the quantum program when sampling. Each key should be the name of a parameter in the quantum program (parameters of the main function), and the value should be the value to set for that parameter.	`None`

Returns:

Type	Description
`ExecutionDetails`	The result of the sampling.

submit_batch_estimate

submit_batch_estimate(
    hamiltonian: Hamiltonian,
    parameters: list[ExecutionParams],
) -> ExecutionJob

Initiates an execution job with the batch_estimate primitive.

This is a non-blocking version of batch_estimate: it gets the same parameters and initiates the same execution job, but instead of waiting for the result, it returns the job object immediately.

Parameters:

Name	Type	Description	Default
`hamiltonian`	`Hamiltonian`	The Hamiltonian to estimate the expectation value of.	required
`parameters`	`list[ExecutionParams]`	A list of the parameters for each iteration. Each item is a dictionary where each key should be the name of a parameter in the quantum program (parameters of the main function), and the value should be the value to set for that parameter.	required

Returns:

Type	Description
`ExecutionJob`	The execution job.

submit_batch_sample

submit_batch_sample(
    parameters: list[ExecutionParams],
) -> ExecutionJob

Initiates an execution job with the batch_sample primitive.

This is a non-blocking version of batch_sample: it gets the same parameters and initiates the same execution job, but instead of waiting for the result, it returns the job object immediately.

Parameters:

Name	Type	Description	Default
`parameters`	`list[ExecutionParams]`	A list of the parameters for each iteration. Each item is a dictionary where each key should be the name of a parameter in the quantum program (parameters of the main function), and the value should be the value to set for that parameter.	required

Returns:

Type	Description
`ExecutionJob`	The execution job.

submit_estimate

submit_estimate(
    hamiltonian: Hamiltonian,
    parameters: Optional[ExecutionParams] = None,
) -> ExecutionJob

Initiates an execution job with the estimate primitive.

This is a non-blocking version of estimate: it gets the same parameters and initiates the same execution job, but instead of waiting for the result, it returns the job object immediately.

Parameters:

Name	Type	Description	Default
`hamiltonian`	`Hamiltonian`	The Hamiltonian to estimate the expectation value of.	required
`parameters`	`Optional[ExecutionParams]`	The values to set for the parameters of the quantum program when estimating. Each key should be the name of a parameter in the quantum program (parameters of the main function), and the value should be the value to set for that parameter.	`None`

Returns:

Type	Description
`ExecutionJob`	The execution job.

submit_sample

submit_sample(
    parameters: Optional[ExecutionParams] = None,
) -> ExecutionJob

Initiates an execution job with the sample primitive.

This is a non-blocking version of sample: it gets the same parameters and initiates the same execution job, but instead of waiting for the result, it returns the job object immediately.

Parameters:

Name	Type	Description	Default
`parameters`	`Optional[ExecutionParams]`	The values to set for the parameters of the quantum program when sampling. Each key should be the name of a parameter in the quantum program (parameters of the main function), and the value should be the value to set for that parameter.	`None`

Returns:

Type	Description
`ExecutionJob`	The execution job.

update_execution_preferences

update_execution_preferences(
    execution_preferences: Optional[ExecutionPreferences],
) -> None

Update the execution preferences for the session.

Parameters:

Name	Type	Description	Default
`execution_preferences`	`Optional[ExecutionPreferences]`	The execution preferences to update.	required

Returns:

Type	Description
`None`	None

QuantumProgram

Bases: VersionedModel, CircuitCodeInterface

Methods:

Name	Description
`from_qprog`	Creates a `QuantumProgram` instance from a raw quantum program string.
`get_registers_initialization`
`save_results`	Saves quantum program results as json into a file.
`to_base_program`
`to_program`

Attributes:

Name	Type	Description
`creation_time`	`str`
`data`	`GeneratedCircuitData`
`debug_info`	`Optional[list[FunctionDebugInfoInterface]]`
`execution_primitives_input`	`Optional[PrimitivesInput]`
`hardware_data`	`SynthesisHardwareData`
`initial_values`	`Optional[InitialConditions]`
`model`	`ExecutionModel`
`program_circuit`	`CircuitCodeInterface`
`program_id`	`str`
`synthesis_duration`	`Optional[SynthesisStepDurations]`
`transpiled_circuit`	`Optional[TranspiledCircuitData]`

creation_time

creation_time: str = Field(
    default_factory=_get_formatted_utc_current_time
)

data

data: GeneratedCircuitData

debug_info

debug_info: Optional[list[FunctionDebugInfoInterface]] = (
    Field(default=None)
)

execution_primitives_input

execution_primitives_input: Optional[PrimitivesInput] = (
    Field(default=None)
)

hardware_data

hardware_data: SynthesisHardwareData

initial_values

initial_values: Optional[InitialConditions] = Field(
    default=None
)

model

model: ExecutionModel

program_circuit

program_circuit: CircuitCodeInterface

program_id

program_id: str = Field(default_factory=get_uuid_as_str)

synthesis_duration

synthesis_duration: Optional[SynthesisStepDurations] = (
    Field(default=None)
)

transpiled_circuit

transpiled_circuit: Optional[TranspiledCircuitData] = Field(
    default=None
)

from_qprog

from_qprog(qprog: str) -> QuantumProgram

Creates a QuantumProgram instance from a raw quantum program string.

Parameters:

Name	Type	Description	Default
`qprog`	`str`	The raw quantum program in string format.	required

Returns:

Name	Type	Description
`QuantumProgram`	`QuantumProgram`	The `QuantumProgram` instance.

get_registers_initialization

get_registers_initialization(
    initial_values: InitialConditions,
) -> dict[RegisterName, RegisterInitialization]

save_results

save_results(
    filename: Optional[Union[str, Path]] = None
) -> None

Saves quantum program results as json into a file. Parameters: filename (Union[str, Path]): Optional, path + filename of file. If filename supplied add .json suffix. Returns: None

to_base_program

to_base_program() -> QuantumBaseCode

to_program

to_program(
    initial_values: Optional[InitialConditions] = None,
    instruction_set: Optional[QuantumInstructionSet] = None,
) -> QuantumCode

Preferences

Bases: BaseModel

Preferences for synthesizing a quantum circuit.

Attributes:

Name	Type	Description
`machine_precision`	`int`	Specifies the precision used for quantum operations. Defaults to `DEFAULT_MACHINE_PRECISION`.
`backend_service_provider`	`str`	The provider company or cloud service for the requested backend. Defaults to `None`.
`backend_name`	`str`	The name of the requested backend or target. Defaults to `None`.
`custom_hardware_settings`	`CustomHardwareSettings`	Defines custom hardware settings for optimization. This field is ignored if backend preferences are specified.
`debug_mode`	`bool`	If `True`, debug information is added to the synthesized result, potentially slowing down the synthesis. Useful for executing interactive algorithms. Defaults to `True`.
`output_format`	`List[QuantumFormat]`	Lists the output format(s) for the quantum circuit. Defaults to `[QuantumFormat.QASM]`. `QuantumFormat` Options: - QASM = "qasm" - QSHARP = "qsharp" - QIR = "qir" - IONQ = "ionq" - CIRQ_JSON = "cirq_json" - QASM_CIRQ_COMPATIBLE = "qasm_cirq_compatible"
`pretty_qasm`	`bool`	If `True`, formats OpenQASM 2 outputs with line breaks inside gate declarations, improving readability. Defaults to `True`.
`qasm3`	`Optional[bool]`	If `True`, outputs OpenQASM 3.0 in addition to 2.0, applicable to relevant attributes in `GeneratedCircuit`. Defaults to `None`.
`transpilation_option`	`TranspilationOption`	Sets the transpilation option to optimize the circuit. Defaults to `AUTO_OPTIMIZE`. See `TranspilationOption`
`solovay_kitaev_max_iterations`	`Optional[int]`	Specifies the maximum number of iterations for the Solovay-Kitaev algorithm, if used. Defaults to `None`.
`timeout_seconds`	`int`	Timeout setting for circuit synthesis in seconds. Defaults to `300`.
`optimization_timeout_seconds`	`Optional[int]`	Specifies the timeout for optimization in seconds, or `None` for no optimization timeout. This will still adhere to the overall synthesis timeout. Defaults to `None`.
`random_seed`	`int`	Random seed for circuit synthesis.

Raises:

Type	Description
`ClassiqValueError`	If the optimization timeout is greater than or equal to the synthesis timeout. If the `output_format` contains duplicate entries. If `backend_name` is provided without `backend_service_provider` or vice versa.
`ValueError`	If `backend_service_provider` is not valid.

Methods:

Name	Description
`make_output_format_list`
`optimization_timeout_less_than_generation_timeout`
`validate_backend`
`validate_output_format`

Attributes:

Name	Type	Description
`backend_name`	`Optional[Union[PydanticBackendName, AllBackendsNameByVendor]]`
`backend_preferences`	`Optional[BackendPreferences]`	Returns the backend preferences. If the backend preferences are not provided, the function sets the backend preferences according to backend name and provider.
`backend_service_provider`	`Optional[Union[Provider, ProviderVendor, str]]`
`custom_hardware_settings`	`CustomHardwareSettings`
`debug_mode`	`bool`
`machine_precision`	`PydanticMachinePrecision`
`optimization_timeout_seconds`	`Optional[PositiveInt]`
`output_format`	`PydanticConstrainedQuantumFormatList`
`pretty_qasm`	`bool`
`qasm3`	`Optional[bool]`
`random_seed`	`int`
`solovay_kitaev_max_iterations`	`Optional[PositiveInt]`
`synthesize_all_separately`	`bool`
`timeout_seconds`	`PositiveInt`
`transpilation_option`	`TranspilationOption`

backend_name

backend_name: Optional[
    Union[PydanticBackendName, AllBackendsNameByVendor]
] = Field(
    default=None,
    description="Name of the requested backend or target.",
)

backend_preferences

backend_preferences: Optional[BackendPreferences]

Returns the backend preferences. If the backend preferences are not provided, the function sets the backend preferences according to backend name and provider.

backend_service_provider

backend_service_provider: Optional[
    Union[Provider, ProviderVendor, str]
] = Field(
    default=None,
    description="Provider company or cloud for the requested backend.",
)

custom_hardware_settings

custom_hardware_settings: CustomHardwareSettings = Field(
    default_factory=CustomHardwareSettings,
    description="Custom hardware settings which will be used during optimization. This field is ignored if backend preferences are given.",
)

debug_mode

debug_mode: bool = Field(
    default=True,
    description="Add debug information to the synthesized result. Setting this option to False can potentially speed up the synthesis, and is recommended for executing iterative algorithms.",
)

machine_precision

machine_precision: PydanticMachinePrecision = (
    DEFAULT_MACHINE_PRECISION
)

optimization_timeout_seconds

optimization_timeout_seconds: Optional[PositiveInt] = Field(
    default=None,
    description="Optimization timeout in seconds, or None for no optimization timeout (will still timeout when the generation timeout is over)",
)

output_format

output_format: PydanticConstrainedQuantumFormatList = Field(
    default=[QASM],
    description="The quantum circuit output format(s). ",
)

pretty_qasm

pretty_qasm: bool = Field(
    True,
    description="Prettify the OpenQASM2 outputs (use line breaks inside the gate declarations).",
)

qasm3

qasm3: Optional[bool] = Field(
    None,
    description="Output OpenQASM 3.0 instead of OpenQASM 2.0. Relevant only for the `qasm` and `transpiled_circuit.qasm` attributes of `GeneratedCircuit`.",
)

random_seed

random_seed: int = Field(
    default_factory=create_random_seed,
    description="The random seed used for the generation",
)

solovay_kitaev_max_iterations

solovay_kitaev_max_iterations: Optional[PositiveInt] = (
    Field(
        None,
        description="Maximum iterations for the Solovay-Kitaev algorithm (if applied).",
    )
)

synthesize_all_separately

synthesize_all_separately: bool = Field(
    default=False,
    description="If true, all functions will be synthesized separately",
)

timeout_seconds

timeout_seconds: PositiveInt = Field(
    default=300, description="Generation timeout in seconds"
)

transpilation_option

transpilation_option: TranspilationOption = Field(
    default=AUTO_OPTIMIZE,
    description="If true, the returned result will contain a transpiled circuit and its depth",
)

make_output_format_list

make_output_format_list(output_format: Any) -> list

optimization_timeout_less_than_generation_timeout

optimization_timeout_less_than_generation_timeout(
    optimization_timeout_seconds: Optional[PositiveInt],
    info: ValidationInfo,
) -> Optional[PositiveInt]

validate_backend

validate_backend() -> Self

validate_output_format

validate_output_format(
    output_format: PydanticConstrainedQuantumFormatList,
    info: ValidationInfo,
) -> PydanticConstrainedQuantumFormatList

set_preferences

set_preferences(
    serialized_model: SerializedModel,
    preferences: Optional[Preferences] = None,
    **kwargs: Any
) -> SerializedModel

Overrides the preferences of a (serialized) model and returns the updated model.

Parameters:

Name	Type	Description	Default
`serialized_model`	`SerializedModel`	The model in serialized form.	required
`preferences`	`Optional[Preferences]`	The new preferences to be set for the model. Can be passed as keyword arguments.	`None`

Returns:

Name	Type	Description
`SerializedModel`	`SerializedModel`	The updated model with the new preferences applied.

Constraints

Bases: BaseModel

Constraints for the quantum circuit synthesis engine.

This class is used to specify constraints such as maximum width, depth, gate count, and optimization parameters for the synthesis engine, guiding the generation of quantum circuits that satisfy these constraints.

Attributes:

Name	Type	Description
`max_width`	`int`	Maximum number of qubits allowed in the generated quantum circuit. Defaults to `None`.
`max_depth`	`int`	Maximum depth of the generated quantum circuit. Defaults to `None`.
`max_gate_count`	`Dict[TranspilerBasisGates, int]`	A dictionary specifying the maximum allowed count for each type of gate in the quantum circuit. Defaults to an empty dictionary.
`optimization_parameter`	`OptimizationParameterType`	Determines if and how the synthesis engine should optimize the solution. Defaults to `NO_OPTIMIZATION`. See `OptimizationParameterType`

Attributes:

Name	Type	Description
`max_depth`	`Optional[PositiveInt]`
`max_gate_count`	`dict[TranspilerBasisGates, NonNegativeInt]`
`max_width`	`Optional[PositiveInt]`
`model_config`
`optimization_parameter`	`OptimizationParameterType`

max_depth

max_depth: Optional[PositiveInt] = None

max_gate_count

max_gate_count: dict[
    TranspilerBasisGates, NonNegativeInt
] = Field(default_factory=lambda: defaultdict(int))

max_width

max_width: Optional[PositiveInt] = Field(
    default=None,
    description="Maximum number of qubits in generated quantum circuit",
)

model_config

model_config = ConfigDict(extra='forbid')

optimization_parameter

optimization_parameter: OptimizationParameterType = Field(
    default=NO_OPTIMIZATION,
    description="If set, the synthesis engine optimizes the solution according to that chosen parameter",
)

set_constraints

set_constraints(
    serialized_model: SerializedModel,
    constraints: Optional[Constraints] = None,
    **kwargs: Any
) -> SerializedModel

Overrides the constraints of a (serialized) model and returns the updated model.

Parameters:

Name	Type	Description	Default
`serialized_model`	`SerializedModel`	The model in serialized form.	required
`constraints`	`Optional[Constraints]`	The new constraints to be set for the model. Can be passed as keyword arguments.	`None`

Returns:

Name	Type	Description
`SerializedModel`	`SerializedModel`	The updated model with the new constraints applied.

ExecutionPreferences

Bases: BaseModel

Represents the execution settings for running a quantum program. Execution preferences for running a quantum program.

For more details, refer to: `ExecutionPreferences example: ExecutionPreferences..

Attributes:

Name	Type	Description
`noise_properties`	`Optional[NoiseProperties]`	Properties defining the noise in the quantum circuit. Defaults to `None`.
`random_seed`	`int`	The random seed used for the execution. Defaults to a randomly generated seed.
`backend_preferences`	`BackendPreferencesTypes`	Preferences for the backend used to execute the circuit. Defaults to the Classiq Simulator.
`num_shots`	`Optional[PositiveInt]`	The number of shots (executions) to be performed.
`transpile_to_hardware`	`TranspilationOption`	Option to transpile the circuit to the hardware's basis gates before execution. Defaults to `TranspilationOption.DECOMPOSE`.
`job_name`	`Optional[str]`	The name of the job, with a minimum length of 1 character.

Attributes:

Name	Type	Description
`backend_preferences`	`BackendPreferencesTypes`
`job_name`	`Optional[str]`
`noise_properties`	`Optional[NoiseProperties]`
`num_shots`	`Optional[PositiveInt]`
`random_seed`	`int`
`transpile_to_hardware`	`TranspilationOption`

backend_preferences

backend_preferences: BackendPreferencesTypes = (
    backend_preferences_field(backend_name=SIMULATOR)
)

job_name

job_name: Optional[str] = Field(
    min_length=1, description="The job name", default=None
)

noise_properties

noise_properties: Optional[NoiseProperties] = Field(
    default=None,
    description="Properties of the noise in the circuit",
)

num_shots

num_shots: Optional[PositiveInt] = Field(default=None)

random_seed

random_seed: int = Field(
    default_factory=create_random_seed,
    description="The random seed used for the execution",
)

transpile_to_hardware

transpile_to_hardware: TranspilationOption = Field(
    default=DECOMPOSE,
    description="Transpile the circuit to the hardware basis gates before execution",
    title="Transpilation Option",
)

set_execution_preferences

set_execution_preferences(
    serialized_model: SerializedModel,
    execution_preferences: Optional[
        ExecutionPreferences
    ] = None,
    **kwargs: Any
) -> SerializedModel

Overrides the execution preferences of a (serialized) model and returns the updated model.

Parameters:

Name	Type	Description	Default
`serialized_model`	`SerializedModel`	A serialization of the defined model.	required
`execution_preferences`	`Optional[ExecutionPreferences]`	The new execution preferences to be set for the model. Can be passed as keyword arguments.	`None`

Returns: SerializedModel: The model with the attached execution preferences.

For more examples please see: set_execution_preferences

jobs

Classes:

Name	Description
`ClassiqExecutionResultError`
`ExecutionJob`

Functions:

Name	Description
`get_execution_jobs_async`

Attributes:

Name	Type	Description
`get_execution_jobs`

get_execution_jobs

get_execution_jobs = syncify_function(
    get_execution_jobs_async
)

ClassiqExecutionResultError

ClassiqExecutionResultError(primitive: str)

Bases: ClassiqError

ExecutionJob

ExecutionJob(details: _JobDetails)

Methods:

Name	Description
`cancel_async`	Cancels the execution job. This implies the cancellation of any ongoing jobs
`cost`
`from_id`
`from_id_async`
`get_batch_estimate_result`	Returns the job's result as a single batch_estimate result after validation. If the result is not yet available, waits for it.
`get_batch_sample_result`	Returns the job's result as a single batch_sample result after validation. If the result is not yet available, waits for it.
`get_estimate_result`	Returns the job's result as a single estimate result after validation. If the result is not yet available, waits for it.
`get_sample_result`	Returns the job's result as a single sample result after validation. If the result is not yet available, waits for it.
`open_in_ide`
`poll_async`
`rename_async`
`result_async`
`result_value`

Attributes:

Name	Type	Description
`backend_name`	`Optional[str]`
`cancel`
`end_time`	`Optional[datetime]`
`error`	`Optional[str]`
`id`	`str`
`ide_url`	`str`
`name`	`Optional[str]`
`num_shots`	`Optional[int]`
`poll`
`program_id`	`Optional[str]`
`provider`	`Optional[str]`
`rename`
`result`
`start_time`	`datetime`
`status`	`JobStatus`

backend_name

backend_name: Optional[str]

cancel

cancel = syncify_function(cancel_async)

end_time

end_time: Optional[datetime]

error

error: Optional[str]

id

id: str

ide_url

ide_url: str

name

name: Optional[str]

num_shots

num_shots: Optional[int]

poll

poll = syncify_function(poll_async)

program_id

program_id: Optional[str]

provider

provider: Optional[str]

rename

rename = syncify_function(rename_async)

result

result = syncify_function(result_async)

start_time

start_time: datetime

status

status: JobStatus

cancel_async

cancel_async() -> None

Cancels the execution job. This implies the cancellation of any ongoing jobs sent to the provider during this execution job.

The function returns without waiting to the actual cancellation. It is possible to continue polling the job in order to ensure its cancellation, which might not be immediate.

cost

cost(*, verbose: bool = False) -> Union[str, JobCost]

from_id

from_id(id: str) -> ExecutionJob

from_id_async

from_id_async(id: str) -> ExecutionJob

get_batch_estimate_result

get_batch_estimate_result() -> list[EstimationResult]

Returns the job's result as a single batch_estimate result after validation. If the result is not yet available, waits for it.

Returns:

Type	Description
`list[EstimationResult]`	The batch_estimate result of the execution job.

Raises:

Type	Description
`ClassiqExecutionResultError`	In case the result does not contain a single batch_estimate result.
`ClassiqAPIError`	In case the job has failed.

get_batch_sample_result

get_batch_sample_result() -> list[ExecutionDetails]

Returns the job's result as a single batch_sample result after validation. If the result is not yet available, waits for it.

Returns:

Type	Description
`list[ExecutionDetails]`	The batch_sample result of the execution job.

Raises:

Type	Description
`ClassiqExecutionResultError`	In case the result does not contain a single batch_sample result.
`ClassiqAPIError`	In case the job has failed.

get_estimate_result

get_estimate_result() -> EstimationResult

Returns the job's result as a single estimate result after validation. If the result is not yet available, waits for it.

Returns:

Type	Description
`EstimationResult`	The estimate result of the execution job.

Raises:

Type	Description
`ClassiqExecutionResultError`	In case the result does not contain a single estimate result.
`ClassiqAPIError`	In case the job has failed.

get_sample_result

get_sample_result() -> ExecutionDetails

Returns the job's result as a single sample result after validation. If the result is not yet available, waits for it.

Returns:

Type	Description
`ExecutionDetails`	The sample result of the execution job.

Raises:

Type	Description
`ClassiqExecutionResultError`	In case the result does not contain a single sample result.
`ClassiqAPIError`	In case the job has failed.

open_in_ide

open_in_ide() -> None

poll_async

poll_async(timeout_sec: Optional[float] = None) -> None

rename_async

rename_async(name: str) -> None

result_async

result_async(
    timeout_sec: Optional[float] = None,
) -> ResultsCollection

result_value

result_value(*args: Any, **kwargs: Any) -> Any

get_execution_jobs_async

get_execution_jobs_async(
    offset: int = 0, limit: int = 50
) -> list[ExecutionJob]

BackendPreferences

Bases: BaseModel

Preferences for the execution of the quantum program.

For more details, refer to: BackendPreferences

Attributes:

Name	Type	Description
`backend_service_provider`	`str`	Provider company or cloud for the requested backend.
`backend_name`	`str`	Name of the requested backend or target.

Methods:

Name	Description
`batch_preferences`
`is_nvidia_backend`

Attributes:

Name	Type	Description
`backend_name`	`str`
`backend_service_provider`	`ProviderVendor`
`hw_provider`	`Provider`

backend_name

backend_name: str = Field(
    ...,
    description="Name of the requested backend or target.",
)

backend_service_provider

backend_service_provider: ProviderVendor = Field(
    ...,
    description="Provider company or cloud for the requested backend.",
)

hw_provider

hw_provider: Provider

batch_preferences

batch_preferences(
    *, backend_names: Iterable[str], **kwargs: Any
) -> list[BackendPreferences]

is_nvidia_backend

is_nvidia_backend() -> bool

Provider

Bases: StrEnum

This class defines all Providers that Classiq supports. This is mainly used in backend_preferences when specifying where do we want to execute the defined model.

Attributes:

Name	Type	Description
`ALICE_AND_BOB`
`AMAZON_BRAKET`
`AQT`
`AZURE_QUANTUM`
`CLASSIQ`
`GOOGLE`
`IBM_QUANTUM`
`INTEL`
`IONQ`
`IQCC`
`OQC`
`id`	`ProviderIDEnum`

ALICE_AND_BOB

ALICE_AND_BOB = 'Alice & Bob'

AMAZON_BRAKET

AMAZON_BRAKET = 'Amazon Braket'

AQT

AQT = 'AQT'

AZURE_QUANTUM

AZURE_QUANTUM = 'Azure Quantum'

CLASSIQ

CLASSIQ = 'Classiq'

GOOGLE

GOOGLE = 'Google'

IBM_QUANTUM

IBM_QUANTUM = 'IBM Quantum'

INTEL

INTEL = 'Intel'

IONQ

IONQ = 'IonQ'

IQCC

IQCC = 'IQCC'

OQC

OQC = 'OQC'

id

id: ProviderIDEnum

AzureBackendPreferences

Bases: BackendPreferences

This class inherits from BackendPreferences. This is where you specify Azure Quantum preferences. See usage in the Azure Backend Documentation.

Attributes:

Name	Type	Description
`location`	`str`	Azure personal resource region. Defaults to `"East US"`.
`credentials`	`Optional[AzureCredential]`	The service principal credential to access personal quantum workspace. Defaults to `None`.
`ionq_error_mitigation_flag`	`Optional[bool]`	Error mitigation configuration upon running on IonQ through Azure. Defaults to `False`.

Attributes:

Name	Type	Description
`backend_service_provider`	`AZURE_QUANTUM`
`credentials`	`Optional[AzureCredential]`
`ionq_error_mitigation_flag`	`Optional[bool]`
`location`	`str`
`run_through_classiq`	`bool`	Returns: `True` if there are no Azure Credentials.

backend_service_provider

backend_service_provider: AZURE_QUANTUM = Field(
    default=AZURE_QUANTUM
)

credentials

credentials: Optional[AzureCredential] = Field(
    default=None,
    description="The service principal credential to access personal quantum workspace",
)

ionq_error_mitigation_flag

ionq_error_mitigation_flag: Optional[bool] = Field(
    default=False,
    description="Error mitigation configuration upon running on IonQ through Azure.",
)

location

location: str = Field(
    default="East US",
    description="Azure personal resource region",
)

run_through_classiq

run_through_classiq: bool

Returns: True if there are no Azure Credentials. Therefore you will be running through Classiq's credentials.

AzureBackendPreferences

Bases: BackendPreferences

This class inherits from BackendPreferences. This is where you specify Azure Quantum preferences. See usage in the Azure Backend Documentation.

Attributes:

Name	Type	Description
`location`	`str`	Azure personal resource region. Defaults to `"East US"`.
`credentials`	`Optional[AzureCredential]`	The service principal credential to access personal quantum workspace. Defaults to `None`.
`ionq_error_mitigation_flag`	`Optional[bool]`	Error mitigation configuration upon running on IonQ through Azure. Defaults to `False`.

Attributes:

Name	Type	Description
`backend_service_provider`	`AZURE_QUANTUM`
`credentials`	`Optional[AzureCredential]`
`ionq_error_mitigation_flag`	`Optional[bool]`
`location`	`str`
`run_through_classiq`	`bool`	Returns: `True` if there are no Azure Credentials.

backend_service_provider

backend_service_provider: AZURE_QUANTUM = Field(
    default=AZURE_QUANTUM
)

credentials

credentials: Optional[AzureCredential] = Field(
    default=None,
    description="The service principal credential to access personal quantum workspace",
)

ionq_error_mitigation_flag

ionq_error_mitigation_flag: Optional[bool] = Field(
    default=False,
    description="Error mitigation configuration upon running on IonQ through Azure.",
)

location

location: str = Field(
    default="East US",
    description="Azure personal resource region",
)

run_through_classiq

run_through_classiq: bool

Returns: True if there are no Azure Credentials. Therefore you will be running through Classiq's credentials.

AzureCredential

AzureCredential(**data: Any)

Bases: BaseSettings

Represents the credentials and configuration required to authenticate with Azure services.

Attributes:

Name	Type	Description
`tenant_id`	`str`	Azure Tenant ID used to identify the directory in which the application is registered.
`client_id`	`str`	Azure Client ID, also known as the application ID, which is used to authenticate the application.
`client_secret`	`str`	Azure Client Secret associated with the application, used for authentication.
`resource_id`	`PydanticAzureResourceIDType`	Azure Resource ID, including the subscription ID, resource group, and workspace, typically used for personal resources.

Attributes:

Name	Type	Description
`client_id`	`str`
`client_secret`	`str`
`model_config`
`resource_id`	`PydanticAzureResourceIDType`
`tenant_id`	`str`

client_id

client_id: str = Field(description='Azure Client ID')

client_secret

client_secret: str = Field(
    description="Azure Client Secret"
)

model_config

model_config = SettingsConfigDict(
    title="Azure Service Principal Credential",
    env_prefix="AZURE_",
    case_sensitive=False,
    extra="allow",
)

resource_id

resource_id: PydanticAzureResourceIDType = Field(
    description="Azure Resource ID (including Azure subscription ID, resource group and workspace), for personal resource"
)

tenant_id

tenant_id: str = Field(description='Azure Tenant ID')

AzureQuantumBackendNames

Bases: StrEnum

AzureQuantum backend names which Classiq Supports running on.

Attributes:

Name	Type	Description
`IONQ_ARIA_1`
`IONQ_ARIA_2`
`IONQ_QPU`
`IONQ_QPU_FORTE`
`IONQ_SIMULATOR`
`MICROSOFT_ESTIMATOR`
`MICROSOFT_FULLSTATE_SIMULATOR`
`QCI_MACHINE1`
`QCI_NOISY_SIMULATOR`
`QCI_SIMULATOR`
`QUANTINUUM_API_VALIDATOR1_1`
`QUANTINUUM_API_VALIDATOR1_2`
`QUANTINUUM_API_VALIDATOR2_1`
`QUANTINUUM_QPU1_1`
`QUANTINUUM_QPU1_2`
`QUANTINUUM_QPU2`
`QUANTINUUM_SIMULATOR1_1`
`QUANTINUUM_SIMULATOR1_2`
`QUANTINUUM_SIMULATOR2`
`RIGETTI_ANKAA2`
`RIGETTI_ANKAA9`
`RIGETTI_SIMULATOR`

IONQ_ARIA_1

IONQ_ARIA_1 = 'ionq.qpu.aria-1'

IONQ_ARIA_2

IONQ_ARIA_2 = 'ionq.qpu.aria-2'

IONQ_QPU

IONQ_QPU = 'ionq.qpu'

IONQ_QPU_FORTE

IONQ_QPU_FORTE = 'ionq.qpu.forte-1'

IONQ_SIMULATOR

IONQ_SIMULATOR = 'ionq.simulator'

MICROSOFT_ESTIMATOR

MICROSOFT_ESTIMATOR = 'microsoft.estimator'

MICROSOFT_FULLSTATE_SIMULATOR

MICROSOFT_FULLSTATE_SIMULATOR = (
    "microsoft.simulator.fullstate"
)

QCI_MACHINE1

QCI_MACHINE1 = 'qci.machine1'

QCI_NOISY_SIMULATOR

QCI_NOISY_SIMULATOR = 'qci.simulator.noisy'

QCI_SIMULATOR

QCI_SIMULATOR = 'qci.simulator'

QUANTINUUM_API_VALIDATOR1_1

QUANTINUUM_API_VALIDATOR1_1 = 'quantinuum.sim.h1-1sc'

QUANTINUUM_API_VALIDATOR1_2

QUANTINUUM_API_VALIDATOR1_2 = 'quantinuum.sim.h1-2sc'

QUANTINUUM_API_VALIDATOR2_1

QUANTINUUM_API_VALIDATOR2_1 = 'quantinuum.sim.h2-1sc'

QUANTINUUM_QPU1_1

QUANTINUUM_QPU1_1 = 'quantinuum.qpu.h1-1'

QUANTINUUM_QPU1_2

QUANTINUUM_QPU1_2 = 'quantinuum.qpu.h1-2'

QUANTINUUM_QPU2

QUANTINUUM_QPU2 = 'quantinuum.qpu.h2-1'

QUANTINUUM_SIMULATOR1_1

QUANTINUUM_SIMULATOR1_1 = 'quantinuum.sim.h1-1e'

QUANTINUUM_SIMULATOR1_2

QUANTINUUM_SIMULATOR1_2 = 'quantinuum.sim.h1-2e'

QUANTINUUM_SIMULATOR2

QUANTINUUM_SIMULATOR2 = 'quantinuum.sim.h2-1e'

RIGETTI_ANKAA2

RIGETTI_ANKAA2 = 'rigetti.qpu.ankaa-2'

RIGETTI_ANKAA9

RIGETTI_ANKAA9 = 'rigetti.qpu.ankaa-9q-1'

RIGETTI_SIMULATOR

RIGETTI_SIMULATOR = 'rigetti.sim.qvm'

ClassiqBackendPreferences

Bases: BackendPreferences

Represents backend preferences specific to Classiq quantum computing targets.

This class is used to configure the backend options for executing quantum circuits on Classiq's platform. The relevant backend names for Classiq targets are specified in ClassiqSimulatorBackendNames & ClassiqNvidiaBackendNames.

For more details, refer to the Classiq Backend Documentation.

Methods:

Name	Description
`is_nvidia_backend`

Attributes:

Name	Type	Description
`backend_service_provider`	`CLASSIQ`

backend_service_provider

backend_service_provider: CLASSIQ = Field(default=CLASSIQ)

is_nvidia_backend

is_nvidia_backend() -> bool

ClassiqSimulatorBackendNames

Bases: StrEnum

The simulator backends available in the Classiq provider.

Attributes:

Name	Type	Description
`SIMULATOR`
`SIMULATOR_DENSITY_MATRIX`
`SIMULATOR_MATRIX_PRODUCT_STATE`
`SIMULATOR_STATEVECTOR`

SIMULATOR

SIMULATOR = 'simulator'

SIMULATOR_DENSITY_MATRIX

SIMULATOR_DENSITY_MATRIX = 'simulator_density_matrix'

SIMULATOR_MATRIX_PRODUCT_STATE

SIMULATOR_MATRIX_PRODUCT_STATE = (
    "simulator_matrix_product_state"
)

SIMULATOR_STATEVECTOR

SIMULATOR_STATEVECTOR = 'simulator_statevector'

IBMBackendPreferences

Bases: BackendPreferences

Represents the backend preferences specific to IBM Quantum services.

Inherits from BackendPreferences and adds additional fields and validations specific to IBM Quantum backends.

Attributes:

Name	Type	Description
`backend_service_provider`	`IBM_QUANTUM`	Indicates the backend service provider as IBM Quantum.
`access_token`	`Optional[str]`	The IBM Quantum access token to be used with IBM Quantum hosted backends. Defaults to `None`.
`provider`	`IBMBackendProvider`	Specifications for identifying a single IBM Quantum provider. Defaults to a new `IBMBackendProvider`.
`qctrl_api_key`	`Optional[str]`	QCTRL API key to access QCTRL optimization abilities.
`run_through_classiq`	`bool`	Run through Classiq's credentials. Defaults to `False`.

See examples in the IBM Quantum Backend Documentation.

Attributes:

Name	Type	Description
`access_token`	`Optional[str]`
`backend_service_provider`	`IBM_QUANTUM`
`provider`	`IBMBackendProvider`
`qctrl_api_key`	`Optional[str]`
`run_through_classiq`	`bool`

access_token

access_token: Optional[str] = Field(
    default=None,
    description="IBM Quantum access token to be used with IBM Quantum hosted backends",
)

backend_service_provider

backend_service_provider: IBM_QUANTUM = Field(
    default=IBM_QUANTUM
)

provider

provider: IBMBackendProvider = Field(
    default_factory=IBMBackendProvider,
    description="Provider specs. for identifying a single IBM Quantum provider.",
)

qctrl_api_key

qctrl_api_key: Optional[str] = Field(
    default=None,
    description="QCTRL API key to access QCTRL optimization abilities",
)

run_through_classiq

run_through_classiq: bool = Field(
    default=False,
    description="Run through Classiq's credentials",
)

IBMBackendProvider

Bases: BaseModel

Represents the provider specs for identifying an IBM Quantum backend.

Attributes:

Name	Type	Description
`hub`	`str`	hub parameter of IBM Quantum provider. Defaults to `"ibm-q"`.
`group`	`str`	group parameter of IBM Quantum provider. Defaults to `"open"`.
`project`	`str`	project parameter of IBM Quantum provider. Defaults to `"main"`.

Attributes:

Name	Type	Description
`group`	`str`
`hub`	`str`
`project`	`str`

group

group: str = 'open'

hub

hub: str = 'ibm-q'

project

project: str = 'main'

AwsBackendPreferences

Bases: BackendPreferences

AWS-specific backend preferences for quantum computing tasks using Amazon Braket.

This class contains configuration options specific to Amazon Braket, including the AWS role ARN, S3 bucket details, and the folder path within the S3 bucket. It extends the base BackendPreferences class to provide additional properties required for interaction with Amazon Braket.

Attributes:

Name	Type	Description
`backend_service_provider`	`AMAZON_BRAKET`	The service provider for the backend, which is Amazon Braket.
`aws_role_arn`	`PydanticAwsRoleArn`	The Amazon Resource Name (ARN) of the role that will be assumed for execution on your Braket account. This is a required field and should be provided to allow secure and authorized access to AWS resources.
`s3_bucket_name`	`str`	The name of the S3 bucket where results and other related data will be stored. This field should contain a valid S3 bucket name under your AWS account.
`s3_folder`	`PydanticS3BucketKey`	The folder path within the specified S3 bucket. This allows for organizing results and data under a specific directory within the S3 bucket.

For more details, refer to: AwsBackendPreferences examples

Attributes:

Name	Type	Description
`aws_role_arn`	`PydanticAwsRoleArn`
`backend_service_provider`	`AMAZON_BRAKET`
`s3_bucket_name`	`str`
`s3_folder`	`PydanticS3BucketKey`

aws_role_arn

aws_role_arn: PydanticAwsRoleArn = Field(
    description="ARN of the role to be assumed for execution on your Braket account."
)

backend_service_provider

backend_service_provider: AMAZON_BRAKET = Field(
    default=AMAZON_BRAKET
)

s3_bucket_name

s3_bucket_name: str = Field(description='S3 Bucket Name')

s3_folder

s3_folder: PydanticS3BucketKey = Field(
    description="S3 Folder Path Within The S3 Bucket"
)

AmazonBraketBackendNames

Bases: StrEnum

Amazon Braket backend names which Classiq Supports running on.

Attributes:

Name	Type	Description
`AMAZON_BRAKET_ASPEN_11`
`AMAZON_BRAKET_DM1`
`AMAZON_BRAKET_IONQ`
`AMAZON_BRAKET_LUCY`
`AMAZON_BRAKET_M_1`
`AMAZON_BRAKET_SV1`
`AMAZON_BRAKET_TN1`

AMAZON_BRAKET_ASPEN_11

AMAZON_BRAKET_ASPEN_11 = 'Aspen-11'

AMAZON_BRAKET_DM1

AMAZON_BRAKET_DM1 = 'dm1'

AMAZON_BRAKET_IONQ

AMAZON_BRAKET_IONQ = 'IonQ Device'

AMAZON_BRAKET_LUCY

AMAZON_BRAKET_LUCY = 'Lucy'

AMAZON_BRAKET_M_1

AMAZON_BRAKET_M_1 = 'Aspen-M-1'

AMAZON_BRAKET_SV1

AMAZON_BRAKET_SV1 = 'SV1'

AMAZON_BRAKET_TN1

AMAZON_BRAKET_TN1 = 'TN1'

IonqBackendPreferences

Bases: BackendPreferences

Represents the backend preferences specific to IonQ services.

Inherits from BackendPreferences and adds additional fields and configurations specific to IonQ backends

Attributes:

Name	Type	Description
`backend_service_provider`	`IONQ`	Indicates the backend service provider as IonQ.
`api_key`	`PydanticIonQApiKeyType`	The IonQ API key required for accessing IonQ's quantum computing services.
`error_mitigation`	`bool`	A configuration option to enable or disable error mitigation during execution. Defaults to `False`.
`run_through_classiq`	`bool`	Running through Classiq's credentials while using user's allocated budget.

See examples in the IonQ Backend Documentation.

Attributes:

Name	Type	Description
`api_key`	`Optional[PydanticIonQApiKeyType]`
`backend_service_provider`	`IONQ`
`error_mitigation`	`bool`
`run_through_classiq`	`bool`

api_key

api_key: Optional[PydanticIonQApiKeyType] = Field(
    default=None, description="IonQ API key"
)

backend_service_provider

backend_service_provider: IONQ = Field(default=IONQ)

error_mitigation

error_mitigation: bool = Field(
    default=False,
    description="Error mitigation configuration.",
)

run_through_classiq

run_through_classiq: bool = Field(
    default=False,
    description="Running through Classiq's credentials while using user's allocated budget.",
)

IonqBackendNames

Bases: StrEnum

IonQ backend names which Classiq Supports running on.

Attributes:

Name	Type	Description
`ARIA_1`
`ARIA_2`
`FORTE_1`
`HARMONY`
`SIMULATOR`

ARIA_1

ARIA_1 = 'qpu.aria-1'

ARIA_2

ARIA_2 = 'qpu.aria-2'

FORTE_1

FORTE_1 = 'qpu.forte-1'

HARMONY

HARMONY = 'qpu.harmony'

SIMULATOR

SIMULATOR = 'simulator'

ClassiqNvidiaBackendNames

Bases: StrEnum

Classiq's Nvidia simulator backend names.

Attributes:

Name	Type	Description
`SIMULATOR`

SIMULATOR

SIMULATOR = 'nvidia_state_vector_simulator'

GCPBackendPreferences

Bases: BackendPreferences

Represents the backend preferences specific to Google Cloud Platform (GCP) services.

Inherits from BackendPreferences and sets the backend service provider to Google.

Attributes:

Name	Type	Description
`backend_service_provider`	`GOOGLE`	Indicates the backend service provider as Google,

See examples in the Google Cloud Backend Documentation.

Methods:

Name	Description
`is_nvidia_backend`

Attributes:

Name	Type	Description
`backend_service_provider`	`GOOGLE`

backend_service_provider

backend_service_provider: GOOGLE = Field(default=GOOGLE)

is_nvidia_backend

is_nvidia_backend() -> bool

AliceBobBackendPreferences

Bases: BackendPreferences

Backend preferences specific to Alice&Bob for quantum computing tasks.

This class includes configuration options for setting up a backend using Alice&Bob's quantum hardware. It extends the base BackendPreferences class and provides additional parameters required for working with Alice&Bob's cat qubits, including settings for photon dissipation rates, repetition code distance, and the average number of photons.

Attributes:

Name	Type	Description
`backend_service_provider`	`ALICE_BOB`	The service provider for the backend, which is Alice&Bob.
`distance`	`Optional[int]`	The number of times information is duplicated in the repetition code. - Tooltip: Phase-flip probability decreases exponentially with this parameter, bit-flip probability increases linearly. - Supported Values: 3 to 300, though practical values are usually lower than 30. - Default: None.
`kappa_1`	`Optional[float]`	The rate at which the cat qubit loses one photon, creating a bit-flip. - Tooltip: Lower values mean lower error rates. - Supported Values: 10 to 10^5. Current hardware is at ~10^3. - Default: None.
`kappa_2`	`Optional[float]`	The rate at which the cat qubit is stabilized using two-photon dissipation. - Tooltip: Higher values mean lower error rates. - Supported Values: 100 to 10^9. Current hardware is at ~10^5. - Default: None.
`average_nb_photons`	`Optional[float]`	The average number of photons. - Tooltip: Bit-flip probability decreases exponentially with this parameter, phase-flip probability increases linearly. - Supported Values: 4 to 10^5, though practical values are usually lower than 30. - Default: None.
`api_key`	`str`	The API key required to access Alice&Bob's quantum hardware. - Required: Yes.

For more details, refer to the Alice&Bob Backend Documentation.

Attributes:

Name	Type	Description
`api_key`	`Optional[PydanticAliceBobApiKeyType]`
`average_nb_photons`	`Optional[float]`
`backend_service_provider`	`ALICE_BOB`
`distance`	`Optional[int]`
`kappa_1`	`Optional[float]`
`kappa_2`	`Optional[float]`
`parameters`	`dict[str, Any]`

api_key

api_key: Optional[PydanticAliceBobApiKeyType] = Field(
    default=None, description="AliceBob API key"
)

average_nb_photons

average_nb_photons: Optional[float] = Field(
    default=None, description="Average number of photons"
)

backend_service_provider

backend_service_provider: ALICE_BOB = Field(
    default=ALICE_AND_BOB
)

distance

distance: Optional[int] = Field(
    default=None, description="Repetition code distance"
)

kappa_1

kappa_1: Optional[float] = Field(
    default=None,
    description="One-photon dissipation rate (Hz)",
)

kappa_2

kappa_2: Optional[float] = Field(
    default=None,
    description="Two-photon dissipation rate (Hz)",
)

parameters

parameters: dict[str, Any]

AliceBobBackendNames

Bases: StrEnum

Alice & Bob backend names which Classiq Supports running on.

Attributes:

Name	Type	Description
`LOGICAL_EARLY`
`LOGICAL_TARGET`
`PERFECT_QUBITS`
`TRANSMONS`

LOGICAL_EARLY

LOGICAL_EARLY = 'LOGICAL_EARLY'

LOGICAL_TARGET

LOGICAL_TARGET = 'LOGICAL_TARGET'

PERFECT_QUBITS

PERFECT_QUBITS = 'PERFECT_QUBITS'

TRANSMONS

TRANSMONS = 'TRANSMONS'

OQCBackendPreferences

Bases: BackendPreferences

This class inherits from BackendPreferences. This is where you specify OQC preferences.

Attributes:

Name	Type	Description
`username`	`str`	OQC username
`password`	`str`	OQC password

Attributes:

Name	Type	Description
`backend_service_provider`	`OQC`
`password`	`str`
`username`	`str`

backend_service_provider

backend_service_provider: OQC = Field(default=OQC)

password

password: str = Field(description='OQC password')

username

username: str = Field(description='OQC username')

OQCBackendNames

Bases: StrEnum

OQC backend names which Classiq Supports running on.

Attributes:

Name	Type	Description
`LUCY`

LUCY

LUCY = 'Lucy'