利用PhaseLift快速表征线性光网络
Rapid characterisation of linear-optical networks via PhaseLift
摘要：
线性光路是利用光进行经典和量子信息处理的基本构件。特别地，由于其单片结构，集成光子学提供了很大的相位稳定性，并且可以依赖于半导体工业提供的大规模可制造性。基于这种光学电路的新设备有望在机器学习应用中实现更快、更节能的计算，甚至实现经典计算机难以实现的量子算法。然而，这一技术革命需要针对可由数千种光学模式组成的设备的准确且可扩展的认证协议。在这里，我们提出了一种新的技术来重建线性光网络的传输矩阵，这是基于低秩矩阵恢复和凸优化问题的最新进展称为相位提升算法。方便地，我们的表征协议可以用相干经典光源和光电二极管来执行。我们证明了这种方法是强大的噪声和规模有效的模式的数量。我们实验测试所提出的表征协议的可编程集成干涉仪设计的量子信息处理。我们比较了传输矩阵重建与我们的方法对一个更苛刻的重建方案的基础上双光子量子干涉。对于5维随机么正，两次重建获得的矩阵之间的平均电路保真度为0.993。
英文摘要：
Linear-optical circuits are elementary building blocks for classical and quantum information processing with light. In particular, due to its monolithic structure, integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry. New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications and even implementing quantum algorithms intractable for classical computers. However, this technological revolution requires accurate and scalable certification protocols for devices that can be comprised of thousands of optical modes. Here, we present a novel technique to reconstruct the transfer matrix of linear optical networks that is based on the recent advances in low-rank matrix recovery and convex optimisation problems known as PhaseLift algorithms. Conveniently, our characterisation protocol can be performed with a coherent classical light source and photodiodes. We prove that this method is robust to noise and scales efficiently with the number of modes. We experimentally tested the proposed characterisation protocol on a programmable integrated interferometer designed for quantum information processing. We compared the transfer matrix reconstruction obtained with our method against the one provided by a more demanding reconstruction scheme based on two-photon quantum interference. For 5-dimensional random unitaries, the average circuit fidelity between the matrices obtained from the two reconstructions is 0.993.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
