通过强化学习优化电路结构实现变分量子传感自动化
Automating Variational Quantum Sensing through Reinforcement-Learned Circuit Structures
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中文总结 AI 辅助
研究人员提出强化学习框架\textsc{AutoQSense}，通过基于费舍尔信息的目标函数搜索量子传感电路架构，其性能优于固定拟设且适配噪声，为量子传感提供资源感知的硬件兼容方案。
中文摘要 AI 辅助
变分量子传感为高精度参数估计提供了有前景的路径，但其性能强烈依赖于探针制备和测量所用的电路架构。现有方法通常在预定义的拟设（ansatz）内优化连续参数，限制了可访问的设计空间，也限制了对传感任务和硬件约束的适配性。本文提出\textsc{AutoQSense}，这是一种基于强化学习的框架，使用基于费舍尔信息的目标函数搜索电路架构。对于少量子比特系统，单个智能体按顺序构建制备和测量电路；对于更大的系统，分布式方案将子系统的局部电路设计分配给子智能体，将块间纠缠分配给受预算约束的智能体。数值结果表明，学习到的架构可复现已知基准策略，适配退相噪声，在使用更少纠缠门的同时优于固定的硬件高效拟设。这些结果确立了\textsc{AutoQSense}是一种资源感知的自适应且硬件兼容的量子传感方法。
英文摘要
Variational quantum sensing offers a promising route to high-precision parameter estimation, but its performance depends strongly on the circuit architectures used for probe preparation and measurement. Existing approaches typically optimize continuous parameters within predefined ansätze, restricting the accessible design space and limiting adaptation to sensing tasks and hardware constraints. Here, we introduce \textsc{AutoQSense}, a reinforcement-learning framework that searches circuit architectures using Fisher-information-based objectives. For few-qubit systems, a single agent sequentially constructs preparation and measurement circuits. For larger systems, a distributed formulation assigns local circuit design to subsystem agents and inter-block entanglement to a budgeted agent. Numerical results show that the learned architectures recover known benchmark strategies, adapt to dephasing noise, and outperform fixed hardware-efficient ansätze while using fewer entangling gates. These results establish \textsc{AutoQSense} as a resource-aware approach to adaptive and hardware-compatible quantum sensing.
