AI 中文总结
研究连续变量中的量子电路片段，引入相关链接积构建框架，在高斯区域简化形式体系，可从模块化组件构建非马尔可夫过程等，扩展量子梳工具包，为相关量子信息任务提供系统方法。
AI 中文摘要
量子电路常被视为具有固定输入和输出的完整过程。但在许多量子信息任务中，自然对象只是这种电路的一个片段，比如要探测的非马尔可夫噪声源、要插入更大算法的子例程或实施自适应策略的智能体。在有限维度中，链接积提供了一种系统方法来分析此类电路片段如何相互作用和组合。本文为连续变量系统开发了相应框架，引入连续变量电路片段及将其拼接成更大过程的相关链接积。表明在高斯区域形式体系大幅简化，链接积可用协方差矩阵表示有效评估。利用该形式体系从模块化组件构建非马尔可夫过程和自适应智能体 - 环境相互作用。这将量子梳工具包扩展到连续变量，为自适应传感、非马尔可夫噪声缓解和高阶量子电路设计提供了系统方法。
英文摘要
Quantum circuits are often drawn as complete processes, with fixed inputs and outputs. In many quantum-information tasks, however, the natural object is only a fragment of such a circuit: an unknown source of non-Markovian noise to be probed, a subroutine to be inserted into a larger algorithm, or an agent implementing an adaptive strategy. In finite dimensions, the link product provides a systematic means to analyze how such circuit fragments interact and compose. Here, we develop the corresponding framework for continuous-variable systems. We introduce continuous-variable circuit fragments and associated link products that stitch such fragments together into larger processes. We show that the formalism simplifies substantially in the Gaussian regime, where link products can be evaluated efficiently using covariance-matrix representations. We use the formalism to construct non-Markovian processes and adaptive agent-environment interactions from modular components. This extends the quantum-comb toolkit to continuous variables, providing systematic methods for adaptive sensing, non-Markovian noise mitigation, and higher-order quantum circuit design.
