基于噪声测量的量子纠缠机器学习
Machine Learning of Quantum Entanglement from Noisy Measurements
浏览论文内容
中文总结 AI 辅助
研究将机器学习算法用于偏振纠缠光子对中量子纠缠的识别与定量表征，基于含泊松散粒噪声的SIC-POVM测量数据，用多种监督算法直接分类可分态与纠缠态，还探索相关技术，结果显示ML方法准确率高，或可为传统量子态分析提供有效替代。
中文摘要 AI 辅助
在这项工作中，我们研究机器学习（ML）算法在偏振纠缠光子对中量子纠缠的识别和定量表征方面的应用。分析基于模拟的对称、信息完备、正定算符值测量（SIC-POVM）测量数据，其中每个两比特状态由对应于实验可获取的符合计数的16维测量向量表示。生成的SIC-POVM测量数据包含泊松散粒噪声。多种监督ML算法，包括逻辑回归、k近邻、决策树、支持向量机和随机森林，被用于直接从原始测量数据对可分态和纠缠态进行分类，无需明确的密度矩阵重建或使用传统可分性标准。该研究还探索了聚类方法和非线性回归技术以估计连续纠缠度量。结果表明，即使在训练条件极其有限的情况下，ML方法也能实现非常高的分类准确率。这些发现表明，在模拟泊松噪声条件下，ML可能为传统量子态分析提供一种有效的替代方法。
英文摘要
In this work, we investigate the application of Machine Learning (ML) algorithms to the identification and quantitative characterization of quantum entanglement in polarization-entangled photon pairs. The analysis is based on simulated symmetric, informationally complete, positive operator-valued measure (SIC-POVM) measurement data, where each two-qubit state is represented by a 16-dimensional measurement vector corresponding to experimentally accessible coincidence counts. The generated SIC-POVM measurement data include Poissonian shot noise. Several supervised ML algorithms, including Logistic Regression, k-Nearest Neighbors, Decision Trees, Support Vector Machines, and Random Forests, are applied to the classification of separable and entangled states directly from raw measurement data, without explicit density matrix reconstruction or the use of conventional separability criteria. The study additionally explores clustering methods and nonlinear regression techniques for estimating continuous entanglement measures. The obtained results demonstrate that ML methods can achieve very high classification accuracy, even under extremely limited training conditions. These findings indicate that ML may provide an efficient alternative to conventional quantum-state analysis under simulated Poissonian noise conditions.
