qlauncher.routines.qiskit.algorithms.qiskit_native

qlauncher.routines.qiskit.algorithms.qiskit_native#

Algorithms for Qiskit routines

Summary#

Classes:

FALQON

Algorithm class with FALQON.

QAOA

Algorithm class with QAOA.

QiskitOptimizationAlgorithm

Abstract class for Qiskit optimization algorithms

Functions:

commutator

Commutator

Reference#

class qlauncher.routines.qiskit.algorithms.qiskit_native.QiskitOptimizationAlgorithm(**alg_kwargs)[source]#

Bases: Algorithm

Abstract class for Qiskit optimization algorithms

make_tag(problem: Problem, backend: QiskitBackend) → str[source]#
get_processing_times(tag: str, primitive: BasePrimitive) → None | tuple[list, list, int][source]#
qlauncher.routines.qiskit.algorithms.qiskit_native.commutator(op_a: SparsePauliOp, op_b: SparsePauliOp) → SparsePauliOp[source]#

Commutator

class qlauncher.routines.qiskit.algorithms.qiskit_native.QAOA(p: int = 1, optimizer: Optimizer | None = None, alternating_ansatz: bool = False, aux=None, **alg_kwargs)[source]#

Bases: QiskitOptimizationAlgorithm

Algorithm class with QAOA.

Parameters:

  • p (int) – The number of QAOA steps. Defaults to 1.

  • optimizer (Optimizer | None) – Optimizer used during algorithm runtime. If set to None turns into COBYLA. Defaults to None,

  • alternating_ansatz (bool) – Whether to use an alternating ansatz. Defaults to False. If True, it’s recommended to provide a mixer_h to alg_kwargs.

  • aux – Auxiliary input for the QAOA algorithm.

  • **alg_kwargs – Additional keyword arguments for the base class.

name#

The name of the algorithm.

Type:

str

aux#

Auxiliary input for the QAOA algorithm.

p#

The number of QAOA steps.

Type:

int

optimizer#

Optimizer used during algorithm runtime.

Type:

Optimizer

alternating_ansatz#

Whether to use an alternating ansatz.

Type:

bool

parameters#

List of parameters for the algorithm.

Type:

list

mixer_h#

The mixer Hamiltonian.

Type:

SparsePauliOp | None

property setup: dict#
parse_samplingVQEResult(res: SamplingVQEResult, res_path) → dict[source]#
run(problem: Problem, backend: Backend, formatter: Callable) → Result[source]#

Runs the QAOA algorithm

construct_result(result: dict) → Result[source]#
get_bitstring(result) → str[source]#
class qlauncher.routines.qiskit.algorithms.qiskit_native.FALQON(driver_h: SparsePauliOp | None = None, delta_t: float = 0.03, beta_0: float = 0.0, max_reps: int = 20)[source]#

Bases: QiskitOptimizationAlgorithm

Algorithm class with FALQON.

Parameters:

  • driver_h (Operator | None) – The driver Hamiltonian for the problem.

  • delta_t (float) – The time step for the evolution operators.

  • beta_0 (float) – The initial value of beta.

  • n (int) – The number of iterations to run the algorithm.

  • **alg_kwargs – Additional keyword arguments for the base class.

driver_h#

The driver Hamiltonian for the problem.

Type:

Operator | None

delta_t#

The time step for the evolution operators.

Type:

float

beta_0#

The initial value of beta.

Type:

float

n#

The number of iterations to run the algorithm.

Type:

int

cost_h#

The cost Hamiltonian for the problem.

Type:

Operator | None

n_qubits#

The number of qubits in the problem.

Type:

int

parameters#

The list of algorithm parameters.

Type:

list[str]

property setup: dict#
run(problem: Problem, backend: QiskitBackend, formatter: Callable) → Result[source]#

Runs the FALQON algorithm

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