qrisp.vqe.VQEProblem.train_function#

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

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 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:

callable: 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.