Coherent advantage in the computational expressivity of excitonic networks

Abstract

The rising energy consumption of AI has generated interest in physical systems as alternative substrates for trainable computation. Recent experimental advances have enabled precise control over the couplings between molecular chromophores, which give rise to coherent excitation dynamics. Here, we study driven-dissipative excitonic networks as a computational platform, where the intersite couplings define the input and the steady state defines the output. We show that coherence enables computational expressivity to scale with network size, analogous to artificial neural networks. Both this scaling and the overall expressivity are suppressed by strong dephasing. Our work establishes coherence as a resource for expressive computation in nonequilibrium quantum systems.