qrisp.cold.DCQOProblem.run#
- DCQOProblem.run(qarg, N_steps, T, method, N_opt=None, CRAB=False, optimizer='COBYQA', objective='agp_coeff_magnitude', bounds=(), options={}, mes_kwargs={}, precision=0.01, exp_value_backend=None)[source]#
Run the specific DCQO problem instance with given quantum arguments, number of timesteps, evolution time and method.
There is also the option to choose if parameter optimization via the expectation value objective function should be done via a simulator or real quantum backend. If the user chooses a quantum backend this iterative optimization can potentially use a lot of computing time.
- Parameters:
- qargQuantumVariable
The argument to which the DCQO circuit is applied.
- N_stepsint
Number of time steps for the simulation.
- Tfloat
Evolution time for the simulation.
- methodstr
Method to solve the QUBO with. Either
LCDorCOLD.- N_optint
Number of optimization parameters in
H_control.- CRABbool
If
True, the CRAB optimization method is being used. The default isFalse.- optimizerstr, optional
Specifies the SciPy optimization routine. We set the default to
Powell.- optionsdict
A dictionary of solver options.
- objectivestr
The objective function to be minimized (
exp_value,agp_coeff_magnitude). Default isagp_coeff_magnitude.- boundstuple
The parameter bounds for the optimizer. Default is (-2, 2).
- optionsdict
Additional options for the Scipy solver.
- mes_kwargsdict, optional
The keyword arguments for the measurement function. Default is an empty dictionary.
- precisionfloat, optional
Precision for expectation value calculations. Default is 0.01.
- exp_value_backendBackendLike, optional
Backend for expectation value calculations, if
exp_valueis used as objective function. If provided, uses measurement-based expectation value with this backend. Default is the Qrisp statevector simulator.
- Returns:
- res_dictdict
The optimal result after running DCQO problem for a specific problem instance. It contains the measurement results after applying the optimal DCQO circuit to the quantum argument.