发表机构
Università degli Studi di Milano（米兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出利用分数阶导数显式建模非马尔可夫耗散的量子储层计算框架，通过调节信息回流提升短期记忆与预测精度，将系统-环境相互作用转化为计算资源。
AI 中文摘要
开放系统动力学的控制为利用量子信息处理顺序任务提供了强大的机制。虽然量子储层计算通常依赖马尔可夫耗散来处理顺序数据，但非马尔可夫记忆效应的计算作用在很大程度上仍未得到探索。我们引入了一个量子储层计算框架，其中非马尔可夫性通过分数阶导数被显式建模和调节。通过采用分数阶时间从属，我们生成可调的重尾弛豫动力学，该动力学控制系统与其环境之间的信息回流。非马尔可夫信息回流调节系统的整体时间保留，最大化短期线性记忆容量。在最优运行区域内，分数阶非马尔可夫性将储层记忆重新分配至近期输入，提高了短期延迟下的线性记忆容量和非线性预测精度，但以长期时间保留为代价。系统-环境相互作用被证明不仅是量子储层计算的要求，而且是一种主动资源，将记忆保留直接嵌入量子演化中。
英文摘要
The control of open-system dynamics provides a powerful mechanism for using quantum information to process sequential tasks. While quantum reservoir computing typically relies on Markovian dissipation to process sequential data, the computational role of non-Markovian memory effects remains largely unexplored. We introduce a framework for quantum reservoir computing where non-Markovianity is explicitly modeled and regulated using fractional derivatives. By employing fractional time subordination, we generate tunable, heavy-tailed relaxation dynamics that govern the information backflow between the system and its environment. Non-Markovian information backflow regulates the overall temporal retention of the system, maximizing short-term linear memory capacity. Within an optimal operating regime, fractional non-Markovianity redistributes the reservoir memory towards recent inputs, improving both linear memory capacity and nonlinear prediction accuracy at short delays, at the expense of long-range temporal retention. System-environment interaction proves being not merely a requirement for quantum reservoir computing, but an active resource that embeds memory retention directly into the quantum evolution.
Comments9 pages, 5 figures
