发表机构
University of Manchester; Department of Physics & Astronomy, University of Manchester; Centre for Quantum Science and Engineering, University of Manchester; Department of Mathematics, University of Manchester（曼彻斯特大学; 曼彻斯特大学物理与天文系; 曼彻斯特大学量子科学与工程中心; 曼彻斯特大学数学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究量子自适应智能体，引入路由-截断-修复过程，将熵量子记忆优势转化为内存维度降低，通过时间矩阵乘积态表示等操作，经保真度-散度证书量化权衡，实现基准自适应过程的维度显著降低且保持行为高保真。
AI 中文摘要
自适应智能体通过利用过去输入刺激和输出动作的记忆来实现复杂的反应行为。量子自适应智能体在存储信息方面比最优经典智能体少，但这不一定转化为必须物理实现的内存维度的降低。我们引入了一种路由-截断-修复过程，将熵量子记忆优势转化为内存维度的降低。通过智能体路由参考输入过程会产生一个时间矩阵乘积态表示，其规范键与智能体的记忆相关。截断此键并局部修复由此产生的动力学，会产生一个更小的、物理上有效的智能体，它仍然能够响应任意输入序列。保真度-散度证书量化了准确性和内存维度之间的权衡。基准自适应过程在保持高保真度的基础行为的同时，实现了显著的维度降低。这些结果建立了一条从熵记忆优势到实用的、维度降低的自适应量子智能体的途径。
英文摘要
Adaptive agents realise complex reactive behaviours by using a memory of past input stimuli and output actions to guide structured future responses. Quantum adaptive agents can operate while storing less information in memory than optimal classical counterparts; yet, this does not necessarily translate into a reduced dimension of the memory that must be physically realised. We introduce a route-truncate-repair procedure that can convert entropic quantum memory advantages into reductions in memory dimension. Routing a reference input process through an agent yields a temporal matrix product state representation whose canonical bond is identified with the joint reference--agent memory. Truncating the agent's share of this bond and locally repairing the resulting dynamics produces a smaller, physically-valid agent that remains capable of responding to arbitrary input sequences. A fidelity-divergence certificate quantifies the resulting trade-off between accuracy and memory dimension. Benchmark adaptive processes exhibit substantial dimension reduction whilst preserving the underlying behaviour with high fidelity. The construction extends to feedback and coherent quantum interactions, with accuracy guarantees under arbitrary adaptive interrogation for a class of agents. For a resettable clock, four quantum memory dimensions attain an input--output fidelity divergence rate first matched with eleven states in our search over classical reduced models. These results establish a route from entropic memory advantages to practical, dimension-reduced adaptive quantum agents.
Commentsv2 12 page + 40 page supplement
