qrisp.vqe.VQEProblem.run

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

Run VQE for the specific problem instance.

Parameters:

qargQuantumVariable or callable

The argument to which the VQE circuit is applied, or a function returning a QuantumVariable to which the VQE circuit is applied.

depthint

The amount of VQE ansatz layers.

mes_kwargsdict, optional

The keyword arguments for the expectation_value 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_pointndarray, shape (n,), optional

Specifies the initial optimization parameters.

optimizerstr, optional

Specifies the SciPy optimization routine. Available are, e.g., COBYLA, COBYQA, Nelder-Mead. The Default is COBYLA. In tracing mode (i.e. Jasp) Jax-traceable optimization routines must be utilized. Available are COBYLA, SPSA.

optionsdict

A dictionary of solver options.

Returns:

float or jax.Array

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