发表机构
The Hong Kong Polytechnic University; Nanyang Technological University; National University of Singapore（香港理工大学; 南洋理工大学; 新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种无需主动调谐的集成光子量子储备池，实现非线性映射与时间记忆，在零功耗下完成分类、预测等任务，为低功耗大规模量子机器学习提供可扩展范式。
AI 中文摘要
集成光子微处理器提供了高带宽、大规模并行的线性计算，但实现非线性特征映射和时间记忆仍是机器学习的关键挑战。传统方法依赖主动调谐和额外的非线性元件，增加了架构复杂性和功耗开销。在此，我们展示了一种集成光子量子储备池计算机，无需对储备池核心进行主动调谐即可实现非线性映射、衰减记忆和任务通用性。同一芯片支持精确的静态分类、动态预测和稳定的自主预测，在分类和时间推理任务中展现出广泛的适用性。在零偏置状态下，所有片上移相器均未通电，消除了主动控制并将计算功耗降至零，同时保持了有竞争力的性能。这种无源操作突显了一条通往多功能机器学习硬件的可扩展路径，其中大规模光子量子处理器可被重新用作储备池，而无需重新配置其内部光学网络。通过结合量子态编码、多模干涉混合和光子统计读出，该架构为低功耗、大规模量子储备池计算提供了一种基于物理的范式。
英文摘要
Integrated photonic microprocessors provide high-bandwidth, massively parallel linear computation, but realizing nonlinear feature maps and temporal memory remain key challenges for machine learning. Conventional approaches rely on active tuning and additional nonlinear elements, increasing architectural complexity and power overhead. Here we demonstrate an integrated photonic quantum reservoir computer that achieves nonlinear mapping, fading memory, and task versatility without active tuning of the reservoir core. The same chip supports accurate static classification, dynamic prediction, and stable autonomous forecasting, establishing broad utility across both classification and temporal inference tasks. Competitive performance is retained in the zero-bias state, where all on-chip phase shifters are unpowered, eliminating active control and reducing computational power consumption to zero. This passive operation highlights a scalable route to multifunctional machine-learning hardware, where large-scale photonic quantum processors can be repurposed as reservoirs without reconfiguring their internal optical networks. By combining quantum-state encoding, multimode interferometric mixing, and photon-statistical readout, this architecture provides a physically grounded paradigm for low-power, large-scale quantum reservoir computing.
