qrisp.vqe.VQEProblem.train_function

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

This function allows for training of a circuit with a given instance of a VQEProblem. It will then return a function that can be applied to a QuantumVariable, such that it prepares the ground state of the problem Hamiltonian. The function therefore applies a circuit for the problem instance with optimized parameters.

Parameters:

qargQuantumVariable

The argument to which the VQE circuit is applied.

depthint

The amount of VQE layers.

mes_kwargsdict, optional

The keyword arguments for the measurement function. Default is an empty dictionary. By default, the target precision is set to 0.01, and the maximum amount of shots is 100000.

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:

circuit_generatorfunction

A function that can be applied to a QuantumVariable, with optimized parameters for the problem instance. The QuantumVariable then represents the ground state of the problem Hamiltonian.