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.
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BibTeX
@misc{du2026coherent,
title = {{Coherent advantage in the computational expressivity of excitonic networks}},
author = {Matthew Du and Carlos Floyd and Dipti Jasrasaria and Suriyanarayanan Vaikuntanathan},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.10448},
eprint = {2609.10448},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.10448},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Coherent advantage in the computational expressivity of excitonic networks
AU - Matthew Du
AU - Carlos Floyd
AU - Dipti Jasrasaria
AU - Suriyanarayanan Vaikuntanathan
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.10448
UR - https://arxiv.org/abs/2609.10448
AB - 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.
ER -