Coupling Light with Matter for Identifying Dominant Subnetworks

Abstract

We introduce DOMINO, a light-matter computing platform that exploits the full complex amplitude of coupled condensate networks to solve maximum-weight clique problems and reveal hidden indirect correlations in large graphs. By embedding network structure directly into a gain-controlled polaritonic (or photonic) oscillator array, DOMINO performs analog optimization, directly solving the maximum-weight clique problem via the gain-controlled minimisation, through a physically enforced global-intensity constraint, allowing the system to converge rapidly to dominant subnetworks while simultaneously extracting phase, encoded co- and counter-regulation patterns. This gain-based mechanism unlocks capabilities inaccessible to conventional Ising-type simulators: all degrees of freedom (amplitude and phase) participate in the computation, dramatically expanding the class of problems that can be efficiently encoded. Our approach is inherently ultrafast, energy-efficient, and naturally robust to noise, requiring no digital post-processing. Applied to real gene-gene coexpression data, DOMINO reliably identifies biologically meaningful transcription-regulator modules and exposes latent regulatory relationships. Because the method applies generically to any weighted network, it establishes a scalable physical route to solving high-value graph-analytic tasks across biology, finance, social systems, and engineered networks.