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Method to deterministically generate large-amplitude optical cat states

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arXiv 2023年

作者： Li, Zheng-Hong Yu, Fei Li, Zhen-Ya Al-Amri, M. Zubairy, M. Suhail Institute for Quantum Science and Technology Department of Physics Shanghai University Shanghai200444 China Shanghai Key Laboratory of High Temperature Superconductors Shanghai University Shanghai200444 China Department of Physics and Astronomy Texas A&M University College StationTX77843-4242 United States NCQOQI KACST P.O.Box 6086 Riyadh11442 Saudi Arabia Institute of Quantum Technologies and Advanced Computing KACST Riyadh11442 Saudi Arabia

Cat states, as an important resource in the study of macroscopic quantum superposition and quantum information applications, have garnered widespread attention. To date, preparing large-sized optical cat states has remained challenging. We demonstrate that, by utilizing interaction-free measurement and the quantum Zeno effect, even a fragile quantum microscopic system can deterministically control and become entangled with strong light fields, thereby generating large-amplitude optical cat states. During the entire preparation process, our method ensures that the microscopic system functions within a weak field environment, so that its quantum property can be protected. Furthermore, we show that the preparation of cat states is possible even when the quantum microsystem suffers from significant photon loss, provided that optical losses from classical devices are kept low, which implies that the fidelity of the cat state can be enhanced by improvements to and the perfection of the classical optical system. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical systems

Stability and error analysis of imex sav schemes for the magneto-hydrodynamic equations

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arXiv 2021年

作者： Li, Xiaoli Wang, Weilong Shen, Jie School of Mathematics Shandong University JinanShandong250100 China School of Mathematical Sciences Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing Xiamen University XiamenFujian361005 China Department of Mathematics Purdue University West LafayetteIN47907 United States

We construct and analyze first- and second-order implicit-explicit (IMEX) schemes based on the scalar auxiliary variable (SAV) approach for the magneto-hydrodynamic equations. These schemes are linear, only require solving a sequence of linear differential equations with constant coefficients at each time step, and are unconditionally energy stable. We derive rigorous error estimates for the velocity, pressure and magnetic field of the first-order scheme in the two dimensional case without any condition on the time step. Numerical examples are presented to validate the proposed schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Errors

Recent Advances for Quantum Neural Networks in Generative Learning

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arXiv 2022年

作者： Tian, Jinkai Sun, Xiaoyu Du, Yuxuan Zhao, Shanshan Liu, Qing Zhang, Kaining Yi, Wei Huang, Wanrong Wang, Chaoyue Wu, Xingyao Hsieh, Min-Hsiu Liu, Tongliang Yang, Wenjing Tao, Dacheng Artificial Intelligence Research Center Defense Innovation Institute Beijing100071 China Tianjin Artificial Intelligence Innovation Center Tianjin300457 China The Blackett Laboratory Imperial College London LondonSW7 2AZ United Kingdom JD Explore Academy Beijing101111 China School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China The University of Sydney DarlingtonNSW2008 Australia Research Institute Taipei Taiwan

Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is through quantum machine learning, especially quantum generative learning. Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts. As such, QGLMs are receiving growing attention from the quantum physics and computer science communities, where various QGLMs that can be efficiently implemented on near-term quantum machines with potential computational advantages are proposed. In this paper, we review the current progress of QGLMs from the perspective of machine learning. Particularly, we interpret these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum autoencoders, as the quantum extension of classical generative learning models. In this context, we explore their intrinsic relation and their fundamental differences. We further summarize the potential applications of QGLMs in both conventional machine learning tasks and quantum physics. Last, we discuss the challenges and further research directions for QGLMs. © 2022, CC BY.

关键词： Quantum theory

A study on CFL conditions for the DG solution of conservation laws on adaptive moving meshes

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arXiv 2021年

作者： Zhang, Min Huang, Weizhang Qiu, Jianxian School of Mathematical Sciences Peking University Beijing100871 China Department of Mathematics University of Kansas LawrenceKS66045 United States School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing Xiamen University Fujian Xiamen361005 China

The selection of time step plays a crucial role in improving stability and efficiency in the Discontinuous Galerkin (DG) solution of hyperbolic conservation laws on adaptive moving meshes that typically employs explicit stepping. A commonly used selection of time step is a direct extension based on Courant-Friedrichs-Levy (CFL) conditions established for fixed and uniform meshes. In this work, we provide a mathematical justification for those time step selection strategies used in practical adaptive DG computations. A stability analysis is presented for a moving mesh DG method for linear scalar conservation laws. Based on the analysis, a new selection strategy of the time step is proposed, which takes into consideration the coupling of the α-function (that is related to the eigenvalues of the Jacobian matrix of the flux and the mesh movement velocity) and the heights of the mesh elements. The analysis also suggests several stable combinations of the choices of the α-function in the numerical scheme and in the time step selection. Numerical results obtained with a moving mesh DG method for Burgers’ and Euler equations are presented. For comparison purpose, numerical results obtained with an error-based time step-size selection strategy are also *** Codes 65M50, 65M60 Copyright © 2021, The Authors. All rights reserved.

关键词： Jacobian matrices

Data quality control system and long-term performance monitor of LHAASO-KM2A

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Astroparticle Physics 2025年 164卷

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, D.X. Sun, Q.N. Sun, X.N. Sun, Z.B. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max–Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute & School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Ministry of Education Tibet Lhasa850000 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275 China Sun Yat-sen University Guangdong Zhuhai519000 Chi

The KM2A is the largest sub-array of the Large high Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic-ray and gamma-ray showers. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. For example, according to the observation of the Crab Nebula with KM2A at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be −0.003°±0.005° and 0.001°±0.006° in the R.A. and Dec directions, respectively. © 2024 Elsevier B.V.

关键词： Cosmology

A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction

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Disease Markers 2022年第1期2022卷 7593750页

作者： Xu, He Zheng, Qunli Zhu, Jingshu Xie, Zuoling Cheng, Haitao Li, Peng Ji, Yimu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing 210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing 210023 China Nanjing Center of HPC China Nanjing 210023 China Jiangsu Research Engineering of HPC and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Department of Endocrinology Zhongda Hospital Southeast University Nanjing 210009 China

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge representation learning and deep learning methods, and a disease prediction model is constructed. The model initially constructs the relationship graph between the physical indicator and the test value based on the normal range of human physical examination index. And the human physical examination index for testing value by knowledge representation learning model is encoded. Then, the patient physical examination data is represented as a vector and input into a deep learning model built with self-attention mechanism and convolutional neural network to implement disease prediction. The experimental results show that the model which is used in diabetes prediction yields an accuracy of 97.18% and the recall of 87.55%, which outperforms other machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost). Compared with the best performing random forest method, the recall is increased by 5.34%, respectively. Therefore, it can be concluded that the application of medical knowledge into deep learning through knowledge representation learning can be used in diabetes prediction for the purpose of early detection and assisting diagnosis. © 2022 He Xu et al.

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Protecting Fiber-Optic Quantum key Distribution Sources against Light-Injection Attacks

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PRX Quantum 2022年第4期3卷 040307-040307页

作者： Anastasiya Ponosova Daria Ruzhitskaya Poompong Chaiwongkhot Vladimir Egorov Vadim Makarov Anqi Huang Russian Quantum Center Skolkovo Moscow 121205 Russia NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics Faculty of Science Mahidol University Bangkok 10400 Thailand Quantum technology foundation (Thailand) Bangkok 10110 Thailand Leading research center for Quantum internet ITMO University Birzhevaya line 14 St. Petersburg 199034 Russia SMARTS-Quanttelecom LLC 6 liniya V.O. 59 St. Petersburg 199178 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China

A well-protected and characterized source in a quantum key distribution system is needed for its security. Unfortunately, the source is vulnerable to light-injection attacks, such as Trojan-horse, laser-seeding, and laser-damage attacks, in which an eavesdropper actively injects bright light to hack the source unit. The hacking laser could be a high-power one that can modify properties of components via the laser-damage attack and also further help the Trojan-horse and other light-injection attacks. Here we propose a countermeasure against the light-injection attacks, consisting of an additional sacrificial component placed at the exit of the source. This component should either withstand high-power incoming light while attenuating it to a safe level that cannot modify the rest of the source, or get destroyed into a permanent high-attenuation state that breaks up the line. We demonstrate experimentally that off-the-shelf fiber-optic isolators and circulators have these desired properties, at least under attack by a continuous-wave high-power laser.

关键词： Quantum communication Quantum cryptography

Job Placement Strategy with Opportunistic Resource Sharing for Distributed Deep Learning Clusters

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Job Placement Strategy with Opportunistic Resource Sharing f...

IEEE International Conference on high performance computing and Communications (HPCC)

作者： Hongliang Li Ting Sun Xiang Li Haixiao Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China High Performance Computing Center of Jilin University Changchun China

ISBN: (纸本)9781728176505

Distributed deep learning frameworks train large deep leaning workload with multiple training jobs on shared distributed GPU servers. There are new challenges when scheduling resources for these systems. Modern deep learning training jobs tend to consume large amount of GPU memory. A training job has an iterative nature that causes the memory usage fluctuate overtime. Jobs sharing a host may suffer from significant performance degradation caused by memory overload in runtime. Moreover, even without memory overloads, deep learning training jobs still experience different levels of performance interference when sharing a GPU device. This paper studies these two issues. We introduced an opportunistic memory sharing model to allocate resources for training jobs with time-varying memory requirements. Based on this model, we introduced an opportunistic Job Placement Problem (OJPP) for shared GPU clusters that seeks job placement configurations using minimum number of GPU devices and guarantees user-defined performance requirements. We proposed a greedy algorithm and a heuristic algorithm with computational complexities of $O(n\log n)$ and $O(n^performance\log n)$, respectively, to solve the problem. Extensive experiments are conducted using a GPU cluster to verify the correctness, effectiveness, and the scalability of our approaches. The proposed approach achieved over 80% percent of the standalone performance, in term of average job completion time, with less than 30% extra resources consumption.

关键词： Training Deep learning performance evaluation Greedy algorithms high performance computing Heuristic algorithms Computational modeling

Mechanism of friction-induced chemical reaction high-efficient polishing single crystal 4H-SiC wafer using pure iron

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Tribology International 2024年 193卷

作者： Wu, Min Huang, Hui Wu, Yueqin Xu, Zhiteng Li, Tukun Macleod, Iain Wu, Xiaolei National and Local Joint Engineering Research Center for Intelligent Manufacturing Technology of Brittle Material Products Huaqiao University Xiamen 361021 China Institute of Manufacturing Engineering Huaqiao University Xiamen 361021 China State Key Laboratory of High-Performance Tools Zhengzhou 450001 China EPSRC Future Advanced Metrology Hub Centre for Precision Technologies School of Computing and Engineering University of Huddersfield Huddersfield HD1 3DH United Kingdom IMA Ltd 29 Clay Lane Cheshire WA15 8PJ United Kingdom Zhengzhou Research Institute for Abrasives & Grinding Co. Ltd Zhengzhou 450001 China

This paper proposes a novel method for friction-induced chemical reaction machining of single-crystal 4H-SiC wafers using pure iron. Material Removal Rate (MRR) and surface quality of single-crystal 4H-SiC were studied, and the results demonstrate that both high surface quality and high MRR (Ra: 2.2 nm, MRR: 375 nm/min) were obtained. The mechanism of material removal during friction was investigated by examining the critical roles of friction speed. Solid-state chemical reactions between metal and SiC surfaces can be induced by friction, and the subsequent removal of reactants, resulting in a near-damage-free machined surface. A removal mechanism model of friction was established, which reveals that both the rates of solid-state reaction and reactant removal significantly impact the final removal rate of friction. © 2024 Elsevier Ltd

关键词： Friction-induced chemical reaction Pure iron Removal mechanism SiC