用贝叶斯方法动态优化量子传感，提升单次测量精度。
Adaptive Bayesian Single-Shot Quantum Sensing
- 基于贝叶斯推理在线调整量子探针参数，实现自适应感知。
- 在单次探测场景下，显著提升对物理场的估计精度。
- 适用于多传感器融合，适合高精度量子测量研究者。
量子传感利用量子系统的独特性质，实现时间、磁场、电场、加速度和引力梯度等物理量的测量精度远超经典传感器。然而，识别合适的传感探针和测量方案在经典计算上可能不可行，因需在高维希尔伯特空间中进行优化。在变分量子传感中，通过参数化量子电路（PQC）生成探针，经由量子信道作用于未知物理参数后测量，收集经典数据。传统方法通常采用基于频率学学习准则的离线优化策略。本文提出一种自适应协议，利用贝叶斯推断通过最大化主动信息增益来优化传感策略。该变分方法专为非渐近情形设计，即每个时间步仅使用一次探针，并可扩展支持多个量子传感代理的估计融合。
原文摘要 · Abstract (English)
Quantum sensing harnesses the unique properties of quantum systems to enable precision measurements of physical quantities such as time, magnetic and electric fields, acceleration, and gravitational gradients well beyond the limits of classical sensors. However, identifying suitable sensing probes and measurement schemes can be a classically intractable task, as it requires optimizing over Hilbert spaces of high dimension. In variational quantum sensing, a probe quantum system is generated via a parameterized quantum circuit (PQC), exposed to an unknown physical parameter through a quantum channel, and measured to collect classical data. PQCs and measurements are typically optimized using offline strategies based on frequentist learning criteria. This paper introduces an adaptive protocol that uses Bayesian inference to optimize the sensing policy via the maximization of the active information gain. The proposed variational methodology is tailored for non-asymptotic regimes where a single probe can be deployed in each time step, and is extended to support the fusion of estimates from multiple quantum sensing agents.
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