Community detection in brain connectomes with hybrid quantum computing.

ABSTRACT: Recent advancements in network neuroscience are pointing in the direction of considering the brain as a small-world system with an efficient integration-segregation balance that facilitates different cognitive tasks and functions. In this context, community detection is a pivotal issue in computational neuroscience. In this paper we explored community detection within brain connectomes using the power of quantum annealers, and in particular the Leap's Hybrid Solver in D-Wave. By reframing the modularity optimization problem into a Discrete Quadratic Model, we show that quantum annealers achieved higher modularity indices compared to the Louvain Community Detection Algorithm without the need to overcomplicate the mathematical formulation. We also found that the number of communities detecte

SUBMITTER: Wierzbinski M 

PROVIDER: S-EPMC9977923 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature