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qrisp.qaoa.QAOAProblem.run — documentation

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

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

Parameters:

qargQuantumVariable or QuantumArray or callable

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

depthint

The amount of QAOA layers.

mes_kwargsdict, optional

The keyword arguments for the measurement function. Default is an empty dictionary.

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 are random and tqa. The default is random: The parameters are initialized uniformly at random in the interval \([0,\pi/2]\). For tqa, the parameters are chosen based on the Trotterized Quantum Annealing protocol. If tqa is chosen, and no init_function for the QAOAProblem is specified, the \(\ket{-}^n\) state is prepared (the ground state for the X mixer).

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:

dict or jax.Array

The optimal result after running QAOA problem for a specific problem instance. It contains the measurement results after applying the optimal QAOA circuit to the quantum argument.

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