经典光的量子受限盲源分离
Quantum-Limited Blind Source Separation of Classical Light
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中文总结 AI 辅助
该研究利用量子多参数估计框架，提出传感辅助方法实现经典光的盲源分离，其集体量子测量在弱光 regime 下相比外差层析成像方法更具优势。
中文摘要 AI 辅助
利用量子多参数估计框架，我们研究了通过未知无源线性变换混合的独立热光源的分离问题，这在信号处理中被称为盲源分离。我们提出了一种传感辅助方法，该方法在可编程干涉仪的单元内迭代估计并抑制光学关联。我们表明，集体量子测量相比传统检测方法具有显著优势，通过构造集体测量，其在干涉仪单元内针对各自子问题渐近达到Holevo-Cramér-Rao界。通过在光子网格中系统排列多个此类单元，我们的方案实现了输入多模关联矩阵的原位雅可比对角化。我们将我们的方法与使用外差检测重建完整协方差矩阵并随后在经典计算机上对角化的传统方法进行对比。与该外差层析成像方法的比较表明，我们的传感方法在弱光 regime 中具有特别优势。
英文摘要
Using the framework of quantum multiparameter estimation, we study the problem of separating independent thermal optical sources mixed by an unknown passive linear transformation, which is known as blind source separation in signal processing. We propose a sensing-assisted method that iteratively estimates and suppresses optical correlations directly within the unit cells of a programmable interferometer. We show that collective quantum measurements exhibit a substantial advantage over conventional detection methods by constructing collective measurements that asymptotically achieve the Holevo Cramér--Rao bound in a unit cell of the interferometer for the respective sub-problem. By systematically arranging multiple such cells in a photonic mesh, our proposal implements an in situ Jacobi diagonalization of the input multimode correlation matrix. We contrast our method with a conventional approach where heterodyne detection is used to reconstruct the full covariance matrix and subsequently diagonalize it on a classical computer. A comparison with this heterodyne tomography approach shows that our sensing-based method is particularly advantageous in the weak-light regime.
