quast_decisiontree.core.problem_data_basic_keys

quast_decisiontree.core.problem_data_basic_keys

defines the basic keys known to the decision tree

basic_keys module-attribute

basic_keys = dict(
    instance_file=(str, []),
    problem_instance=(None, []),
    problem_class=(str, []),
    formulation=(str, []),
    qubo_matrix=(
        object,
        [
            lambda x: len(np.shape(x)) == 2,
            lambda x: np.shape(x)[0] == np.shape(x)[1],
        ],
    ),
    qubo_offset=(float, []),
    num_qubits=(int, []),
    algorithm=(str, []),
    reps=(int, []),
    optimizer_type=(str, []),
    optimizer=(None, []),
    backend=(None, []),
    initial_params=((list, NoneType), []),
    solver=(None, []),
    solver_input=(None, []),
    solved=(bool, []),
    ansatz_from=(str, []),
    ansatz_generator=(str, []),
    mixer=(None, []),
    problem_size=(int, []),
    mixer_from=(str, []),
    mixer_type=(str, []),
    num_parameters=(int, []),
    algorithm_setup_node=(str, []),
    cost_operator=(None, []),
    cl_cost_function=(None, [callable]),
    max_iter=(int, [lambda x: x > 0]),
    init_params=(None, []),
    init_type=(str, []),
    hybrid_algorithm=(None, []),
    hybrid_inputs=(dict, []),
    ansatz_function=(None, []),
    ansatz_type=(str, []),
    num_params=(int, [lambda x: x > 0]),
    depth=(int, [lambda x: x > 0]),
    hamiltonian=(None, []),
    shots=(int, [lambda x: x > 0]),
    delta_gamma=(float, [lambda x: x > 0]),
    delta_beta=(float, [lambda x: x > 0]),
)