基于物理的与数据驱动的稀疏自相关数据单光子量子发射器分类方法对比
Physics-Based versus Data-Driven Classification of Single-Photon Quantum Emitters from Sparse Autocorrelation Data
AI总结：
本文对比序列贝叶斯推理、Levenberg-Marquardt拟合与前馈神经网络，研究稀疏光子统计下单光子发射器分类性能，发现两类方法互补，为相关筛选任务提供实用指导。
AI中文摘要：
从大量非均匀候选群体中识别单光子发射器是实现诸多量子应用的关键，这需要测量发射器的二阶自相关函数，其统计可靠性受限于采集时间。机器学习分类器可加速从稀疏数据中识别，但尚未系统研究其相对于基于物理的推理的性能。本文提出用于单光子发射器分类的序列贝叶斯推理方法，并将其与Levenberg-Marquardt拟合及前馈神经网络进行基准测试。我们使用针对六方氮化硼发射器的实际Hanbury Brown-Twiss测量校准的合成训练与测试数据，可在实际噪声和背景条件下对照完全已知的真实发射器数量进行评估。三种方法在积分时间充足时均达到接近完美的高准确率，但在稀疏光子统计下的收敛速度和鲁棒性差异显著：神经网络在短积分时间下鲁棒性最强；贝叶斯分类器以最快速度达到接近完美的准确率，同时保留完全的物理可解释性；Levenberg-Marquardt拟合仍是有价值的完全可解释方法，尽管收敛最慢，但召回率最高。这些结果得出关键结论：无单一方法在所有性能指标上占优；仅依赖单一指标会对分类器性能产生误导，尤其在稀疏光子统计下；基于物理的方法与数据驱动方法是互补而非竞争的。综上，这些发现为选择和组合分类策略以实现可扩展单光子源筛选及其他量子发射器表征任务提供了实用指导。
英文摘要：
Identifying single photon emitters from large, inhomogeneous candidate populations is key to realizing many quantum applications. This requires measuring the emitters second order autocorrelation function, whose statistical reliability is fundamentally limited by acquisition time. Machine learning classifiers can accelerate identification from sparse data, but their performance relative to physics-based inference has not been systematically examined. Here, we introduce sequential Bayesian inference for single photon emitter classification and benchmark it against Levenberg-Marquardt fitting and a feedforward neural network. We use synthetic training and test data calibrated against real Hanbury Brown-Twiss measurements from hexagonal boron nitride emitters, enabling evaluation against an exactly known ground-truth emitter number under realistic noise and background conditions. All three approaches achieve high, near-perfect accuracy with sufficient integration time, but differ greatly in convergence rate and robustness under sparse photon statistics. The neural network is most robust at short integration times. The Bayesian classifier reaches near-perfect accuracy fastest, while retaining full physical interpretability. Levenberg-Marquardt fitting remains a valuable, fully interpretable method, achieving the highest recall despite being the slowest to converge. These results lead to several key conclusions. No single method dominates across all performance metrics. Relying on any one metric alone can give a misleading picture of classifier performance, particularly under sparse photon statistics. Physics-based and data-driven methods are complementary rather than competing approaches. Together, these findings provide practical guidance for selecting and combining classification strategies for scalable single-photon-source screening and other quantum-emitter characterization tasks.
