QAOAProblem#

class QAOAProblem(cost_operator, mixer, cl_cost_function, init_function=None, callback=False)[source]#

Central structure to facilitate treatment of QAOA problems.

This class encapsulates the cost operator, mixer operator, and classical cost function for a specific QAOA problem instance. It also provides methods to set the initial state preparation function, classical cost post-processing function, and optimizer for the problem.

For a quick demonstration, we import the relevant functions from already implemented problem instances:

from networkx import Graph

G = Graph()

G.add_edges_from([[0,3],[0,4],[1,3],[1,4],[2,3],[2,4]])

from qrisp.qaoa import (QAOAProblem, 
                        create_maxcut_cost_operator, 
                        create_maxcut_cl_cost_function,
                        RX_mixer)

maxcut_instance = QAOAProblem(cost_operator = create_maxcut_cost_operator(G),
                               mixer = RX_mixer,
                               cl_cost_function = create_maxcut_cl_cost_function(G))

from qrisp import QuantumVariable

res = maxcut_instance.run(qarg = QuantumVariable(5),
                          depth = 4, 
                          max_iter = 25)

print(res)
#Yields: {'11100': 0.2847, '00011': 0.2847, '10000': 0.0219, '01000': 0.0219, '00100': 0.0219, '11011': 0.0219, '10111': 0.0219, '01111': 0.0219, '00010': 0.02, '11110': 0.02, '00001': 0.02, '11101': 0.02, '00000': 0.0173, '11111': 0.0173, '10010': 0.0143, '01010': 0.0143, '11010': 0.0143, '00110': 0.0143, '10110': 0.0143, '01110': 0.0143, '10001': 0.0143, '01001': 0.0143, '11001': 0.0143, '00101': 0.0143, '10101': 0.0143, '01101': 0.0143, '11000': 0.0021, '10100': 0.0021, '01100': 0.0021, '10011': 0.0021, '01011': 0.0021, '00111': 0.0021}

For an in-depth tutorial, make sure to check out MaxCut QAOA Implementation!

Parameters:

cost_operatorfunction: A function receiving a QuantumVariable or QuantumArray and parameter \(\gamma\). This function performs the application of the cost operator.
mixerfunction: A function receiving a QuantumVariable or QuantumArray and parameter \(\beta\). This function performs the application mixing operator.
cl_cost_functionfunction: The classical cost function for the specific QAOA problem instance, which takes a dictionary of measurement results as input.
init_functionfunction, optional: A function receiving a QuantumVariable or QuantumArray for preparing the inital state. By default, the uniform superposition state \(\ket{+}^n\) is prepared.
callbackBooelan, optional: If True, intermediate results are stored. The default is False.

Methods#

`QAOAProblem.run`(qarg, depth[, mes_kwargs, ...])	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).
`QAOAProblem.train_function`(qarg, depth[, ...])	This function allows for training of a circuit with a given `QAOAProblem` instance.
`QAOAProblem.compile_circuit`(qarg, depth)	Compiles the circuit that is evaluated by the `run` method.
`QAOAProblem.benchmark`(qarg, depth_range, ...)	This method enables convenient data collection regarding performance of the implementation.
`QAOAProblem.set_init_function`(init_function)	Set the initial state preparation function for the QAOA problem.
`QAOAProblem.visualize_cost`()	Visualizes the cost during the optimization process.