On the use of quantum technologies to discover brain structure

The talk will be given by Joan Falco Roget from Sano Centre for Computational Medicine in Kraków.

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 neu-roscience. 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 over-complicate the mathematical formulation. We also found that the number of communities detected in brain connectomes slightly differed while still being biologically interpretable. These promising preliminary results, together with recent findings, strengthen the claim that quantum optimization methods might be a suitable alternative against classical approaches when dealing with community assignment in networks.

References

[1] Wierzbiński, M., Falcó-Roget, J. & Crimi, A. Community detection in brain connectomes with hybrid quantum computing. Sci Rep 13, 3446 (2023). https://www.nature.com/articles/s41598-023-30579-y