Accurate control of light polarization represents a core building block in polarization metrology, imaging, and optical and quantum communications. Voltage-controlled liquid crystals offer an efficient way of polarization transformation. However, common twisted nematic liquid crystals are notorious for lacking an accurate theoretical model linking control voltages and output polarization. An inverse model, which would predict control voltages required to prepare a target polarization, is even more challenging. Here we report both the direct and inverse models based on deep neural networks, radial basis functions, and linear interpolation. We present an inverse-direct compound model solving the problem of control voltages ambiguity. We demonstrate one order of magnitude improvement in accuracy using deep learning compared to the radial basis function method and two orders of magnitude improvement compared to the linear interpolation. Errors of the deep neural network model also decrease faster than the other methods with an increasing number of training data. The best direct and inverse models reach the average infidelities of $4 \times 10^{-4}$ and $2 \times 10^{-4}$, respectively, which is the accuracy level not reported yet. Furthermore, we demonstrate local and remote preparation of an arbitrary single-photon polarization state using the deep learning models. The results will impact the application of twisted-nematic liquid crystals, increasing their control accuracy across the board. The presented bidirectional learning can be used for optimal classical control of complex photonic devices and quantum circuits beyond interpolation.
Quantum Walks Assisted Bidirectional Remote State Preparation
Government College Malappuram（马拉普兰政府学院）；Farook College(Autonomous)（法鲁克学院（自治））；Government Arts and Science College, Meenchanda（梅恩昌达政府艺术与科学学院）；SARBTM Govt. College, Koyilandy（科伊兰迪SARBTM政府学院）
An Optimized Bidirectional Quantum Teleportation Scheme with the use of Bell states
双向量子隐形传态方案是一种双方同时接收对方信息的双向量子通信过程。最近，在文献[1]（Zhou et al.，IEEE Access，7，44269（2019）），六量子比特簇态已被用于双向传送三量子比特纠缠态（Alice）和任意单量子比特未知量子态（Bob）。从实验的角度来看，团簇型纠缠态的制备和维持是非常困难和昂贵的。因此，在本文中，我们设计了一个双向量子隐形传态方案的最佳方案，并表明所提出的方案需要优化的量子资源量。
Optimizing the dynamical preparation of quantum spin lakes on the ruby lattice
Harvard University（哈佛大学）；Harvard Quantum Initiative（哈佛量子倡议）；Cavendish Laboratory, University of Cambridge（剑桥大学卡文迪许实验室）；Ludwig-Maximilians-Universität München（慕尼黑路德维希-马克西米利安大学）；Munich Center for Quantum Science and Technology (MCQST)（慕尼黑量子科学与技术中心）；Boston University（波士顿大学）；Max-Planck-Institut für Physik komplexer Systeme（马普复杂系统物理研究所）
Dynamical preparation of U(1) quantum spin liquids in an analogue quantum simulator
Ludwig-Maximilians-Universität（慕尼黑大学）；Max-Planck-Institut für Quantenoptik（马克斯·普朗克量子光学研究所）；Munich Center for Quantum Science and Technology (MCQST)（慕尼黑量子科学与技术中心）；Technical University of Munich（慕尼黑工业大学）；Harvard University（哈佛大学）；Daegu Gyeongbuk Institute of Science and Technology (DGIST)（大邱庆北科学技术院）
Evidence of several dipolar quasi-invariants in Liquid Crystals
在一个磁量子数为I的N个耦合自旋的封闭量子系统中，大约有（2 I + 1）^N个运动常数。然而，在核磁共振（NMR）中观察到类固体自旋系统中的这种准不变（QI）态的可能性并不是一个严格精确的预测。这项工作的目的是提供实验证据的几个QI，在质子NMR的小自旋团簇，除了那些已经知道的塞曼，和偶极订单（强和弱）。我们探讨了自旋状态准备与Jeener-Broekaert脉冲序列通过分析时域信号产生的这个序列作为一个函数的准备时间，在各种偶极网络。我们观察到，信号可以解释与两个偶极QIs只有在一个范围内的短的准备时间。在较长时间，时域信号具有类似回声的行为。我们研究了它们的多个量子相干性内容的基础上正交的z-基础上，看到这样的状态涉及大量的相关自旋。然后，我们表明，整个准备时间尺度只能通过假设多个QI的发生，我们隔离实验重建。
Adiabatic echo protocols for robust quantum many-body state preparation
University of Innsbruck（因斯布鲁克大学）；Austrian Academy of Sciences（奥地利科学院）；JILA, University of Colorado and National Institute of Standards and Technology（科罗拉多大学与美国国家标准与技术研究院联合物理科学研究所）；Department of Physics, University of Colorado（科罗拉多大学物理系）
Dirac Spin Liquid Candidate in a Rydberg Quantum Simulator
Université Paris-Saclay, Institut d’Optique Graduate School, CNRS, Laboratoire Charles Fabry（巴黎萨克雷大学、高等光学学院、法国国家科学研究中心、夏尔·法布里实验室）；Princeton University（普林斯顿大学）；Harvard University（哈佛大学）；Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；Max-Born-Institut（马克斯·玻恩研究所）；Nanomaterials and Nanotechnology Research Center (CINN-CSIC), Universidad de Oviedo (UO)（纳米材料与纳米技术研究中心（CINN-CSIC）、阿斯图里亚斯大学）；Carnegie Mellon University（卡内基梅隆大学）
Toward Quantum Utility in Correlated Topological Matter: Variational Preparation of Fractional Quantum Hall Manifolds
Donostia International Physics Center (DIPC)（圣塞巴斯蒂安国际物理中心）；University of the Basque Country (UPV/EHU)（巴斯克大学）；Mondragon Unibertsitatea（蒙德拉贡大学）；TECNALIA（泰克纳利亚研究中心）；IMDEA Nanoscience（IMDEA纳米科学研究所）；Centro de Física de Materiales (CFM-MPC), CSIC-UPV/EHU（材料物理中心（CSIC-巴斯克大学））
