qrisp.vqe.VQEProblem.run

VQEProblem.run(qarg, depth, mes_kwargs={}, max_iter=50, init_type='random', init_point=None, optimizer='COBYLA')

Run the specific VQE problem instance with given quantum arguments, depth of VQE circuit, measurement keyword arguments (mes_kwargs) and maximum iterations for optimization (max_iter).

Parameters:

qargQuantumVariable or QuantumArray

The argument to which the VQE circuit is applied.

depthint

The amount of VQE layers.

mes_kwargsdict, optional

The keyword arguments for the get_measurement function. Default is an empty dictionary. By default, the target precision is set to 0.01. Precision refers to how accurately the Hamiltonian is evaluated. The number of shots the backend performs per iteration scales quadratically with the inverse precision.

max_iterint, optional

The maximum number of iterations for the optimization method. Default is 50.

init_typestring, optional

Specifies the way the initial optimization parameters are chosen. Available is random. The default is random: Parameters are initialized uniformly at random in the interval \([0,\pi/2)]\).

init_pointlist[float], optional

Specifies the initial optimization parameters.

optimizerstr, optional

Specifies the optimization routine. Available are, e.g., COBYLA, COBYQA, Nelder-Mead. The Default is COBYLA.

Returns:

energyfloat

The expected value of the Hamiltonian after applying the optimal VQE circuit to the quantum argument.