Coupling Light with Matter for Identifying Dominant Subnetworks

TL;DR

DOMINO is a novel light-matter computing platform that efficiently solves maximum-weight clique problems and uncovers hidden correlations in large graphs by leveraging coupled condensate networks with complex amplitude manipulation.

Contribution

It introduces a physically enforced global-intensity constrained analog optimization method using gain-controlled polaritonic oscillators for network analysis.

Findings

Successfully identifies biologically meaningful gene modules

Reveals latent regulatory relationships in gene coexpression data

Operates with high speed and energy efficiency without digital post-processing

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.