发表机构
Max Planck Institute for the Physics of Complex Systems; School of Physics, Trinity College Dublin（马克斯·普朗克复杂系统物理研究所; 都柏林三一学院物理学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出利用砖墙架构中的双酉电路作为量子储层计算平台，数值验证其能增强记忆效应、非线性处理并抑制噪声，为含噪中等规模量子设备提供实用方案。
AI 中文摘要
量子储层计算（QRC）是一种机器学习方法，利用物理系统（储层）的内部动力学来编码和处理信息。在这项工作中，我们探索了在砖墙架构中使用双酉电路作为QRC平台，该平台非常适合当前含噪中等规模量子设备。双酉电路具有实际和概念上的优势。我们的结果表明，在适当条件下，双酉性可以带来增强的工作模式：我们通过数值验证，它改善了记忆效应和非线性处理，并抵御有限射击噪声，缓解指数集中。此外，双酉性提供了关于算子动力学如何在基于电路的储层中产生记忆和非线性处理的直观图景。
英文摘要
Quantum reservoir computing (QRC) is a machine learning approach which employs the internal dynamics of a physical system (the reservoir) to encode and process information. In this work, we explore the use of dual-unitary circuits in a brickwork architecture as a platform for QRC, well suited to current noisy intermediate-scale quantum devices. Dual-unitary circuits present both practical and conceptual advantages. Our results indicate that, under appropriate conditions, dual-unitarity can lead to an enhanced regime of operation: we numerically verify that it improves memory effects and nonlinear processing, and shields against finite-shot noise, mitigating exponential concentration. Moreover, dual unitarity offers an intuitive picture of how operator dynamics gives rise to memory and nonlinear processing in circuit-based reservoirs.
Comments25 pages, 17 figures. Comments are welcome
