Inferring Quantum Network Topologies using Genetic Optimisation of Indirect Measurements

TL;DR

This paper presents a method using genetic optimization of indirect measurements to accurately infer the topology of quantum networks, enhancing understanding of quantum information propagation.

Contribution

It introduces a genetic algorithm-based approach for reconstructing quantum network topologies from probe data, demonstrating high success rates and scalability.

Findings

High success rate in topology reconstruction

Increased probes reduce computational complexity

Tradeoff between number of probes and computational resources

Abstract

The characterisation of quantum networks is fundamental to understanding how energy and information propagates through complex systems, with applications in control, communication, error mitigation and energy transfer. In this work, we explore the use of external probes to infer the network topology in the context of continuous-time quantum walks, where a single excitation traverses the network with a pattern strongly influenced by its topology. The probes act as decay channels for the excitation, and can be interpreted as performing an indirect measurement on the network dynamics. By making use of a Genetic Optimisation algorithm, we demonstrate that the data collected by the probes can be used to successfully reconstruct the topology of any quantum network with high success rates, where performance is limited only by computational resources for large network sizes. Moreover, we show that increasing the number of probes significantly simplifies the reconstruction task, revealing a tradeoff between the number of probes and the required computational power.