Control and characterization of networks are paramount steps in the development of many quantum technologies. Even for moderate-sized networks, this amounts to exploring an extremely vast parameter space in search for the couplings defining the network topology. Here, we explore the use of a genetic algorithm to retrieve the topology of a network from the measured probability distribution obtained from the evolution of a continuous-time quantum walk on the network. We show that we can successfully retrieve the topology of different networks with efficiencies above 70% in all the examined scenarios and that the algorithm is capable of efficiently retrieving the required information even in the presence of noise.
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