Identifying network topologies via quantum walk distributions

TL;DR

This paper demonstrates that a genetic algorithm can effectively identify network topologies from quantum walk distributions, even with noisy data, aiding quantum technology development.

Contribution

It introduces a novel method using genetic algorithms to determine network topology from quantum walk data, addressing noise robustness.

Findings

Genetic algorithm accurately retrieves network topology from quantum walk distributions.

Method remains effective despite noisy measurement data.

Provides a scalable approach for quantum network characterization.

Abstract

Control and characterization of networks is a paramount step for the development of many quantum technologies. Even for moderate-sized networks, this amounts to explore an extremely vast parameters 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. Our result shows that the algorithm is capable of efficiently retrieving the required information even in the presence of noise.