发表机构
University of Chicago; The James Franck Institute, University of Chicago（芝加哥大学; 詹姆斯·弗兰克研究所，芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究驱动-耗散激子网络作为计算平台，证明相干性使计算表达性随网络规模扩展，类似人工神经网络，且强退相会抑制该扩展，确立了相干性作为非平衡量子系统中表达性计算的资源。
AI 中文摘要
人工智能日益增长的能源消耗引发了人们对物理系统作为可训练计算替代基板的兴趣。最近的实验进展使得能够精确控制分子发色团之间的耦合，这些耦合产生了相干激发动力学。在此，我们研究驱动-耗散激子网络作为计算平台，其中位点间耦合定义输入，稳态定义输出。我们证明相干性使得计算表达性随网络规模扩展，类似于人工神经网络。这种扩展以及整体表达性都被强退相抑制。我们的工作确立了相干性作为非平衡量子系统中表达性计算的资源。
英文摘要
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.
