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Robust second-order correlation of twin parametric beams generated by amplified spontaneous parametric down-conversion

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Chinese Optics Letters 2020年第12期18卷 63-67页

作者： Kunpeng Jia Xiaohan Wang Xinjie LV Ping Xu Zhenlin Wang ZhiWei Wuang Gang Zhao Yan-Xiao Gong Zhenda Xie Shining Zhu National Laboratory of Solid State Microstructures School of PhysicsSchool of Electronic Science and EngineeringCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China Institute for Quantum Information and State Key Laboratory of High Performance Computing.College of Computing National University of Defense TechnologyChangsha 410073China Mesoscopic Optics and Quantum Electronics Laboratory University of California Los AngelesCciliforniaCA 90095USA

We report an observation of the second-order correlation between twin beams generated by amplified spontaneous parametric down-conversion operating above threshold with kilowatt-level peak power, from a periodically poled Li Ta O3 crystal via a single-pass scheme. Photocurrent correlation was measured because of the bright photon streams, with raw visibility of 37.9% or 97.3% after electronic filtering. As expected in our theory, this correlation is robust and insensitive to parametric gain and detection loss, enabling important applications in optical communications, precision measurement, and nonlocal imaging.

关键词： amplified spontaneous parametric down-con version robust second-order correlation high-gain twin beams

Preparing a commercial quantum key distribution system for certification against implementation loopholes

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arXiv 2023年

作者： Makarov, Vadim Abrikosov, Alexey Chaiwongkhot, Poompong Fedorov, Aleksey K. Huang, Anqi Kiktenko, Evgeny Petrov, Mikhail Ponosova, Anastasiya Ruzhitskaya, Daria Tayduganov, Andrey Trefilov, Daniil Zaitsev, Konstantin Russian Quantum Center Skolkovo Moscow121205 Russia Vigo Quantum Communication Center University of Vigo VigoE-36310 Spain NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Bangkok10110 Thailand QRate Skolkovo Moscow143026 Russia Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Steklov Mathematical Institute Russian Academy of Sciences Moscow119991 Russia atlanTTic Research Center University of Vigo VigoE-36310 Spain School of Telecommunication Engineering Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain National Research University Higher School of Economics Moscow101000 Russia

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarisation encoding, qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and post-processing. We analyse the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure. Copyright © 2023, The Authors. All rights reserved.

关键词： quantum cryptography

Tensor Network Based Finite-Size Scaling for Two-Dimensional Classical Models

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arXiv 2023年

作者： Huang, Ching-Yu Chan, Sing-Hong Kao, Ying-Jer Chen, Pochung Department of Applied Physics Tunghai University Taichung40704 Taiwan Department of Physics National Tsing Hua University Hsinchu30013 Taiwan Department of Physics National Taiwan University Taipei10607 Taiwan Center for Quantum Science and Technology National Taiwan University Taipei10607 Taiwan National Center for High-Performance Computing Hsinchu City30076 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Frontier Center for Theory and Computation National Tsing Hua University Hsinchu30013 Taiwan

We propose a scheme to perform tensor network based finite-size scaling analysis for two-dimensional classical models. In the tensor network representation of the partition function, we use higher-order tensor renormalization group (HOTRG) method to coarse grain the weight tensor. The renormalized tensor is then used to construct the approximated transfer matrix of an infinite strip of finite width. By diagonalizing the transfer matrix we obtain the correlation length, the magnetization, and the energy density which are used in finite-size scaling analysis to determine the critical temperature and the critical exponents. As a benchmark we study the two-dimensional classical Ising model. We show that the critical temperature and the critical exponents can be accurately determined. With HOTRG bond dimension D = 70, the absolute errors of the critical temperature Tc and the critical exponent ν, β are at the order of 10−7, 10−5, 10−4 respectively. Furthermore, the results can be systematically improved by increasing the bond dimension of the HOTRG method. Finally, we study the length scale induced by the finite cut-off in bond dimension and elucidate its physical meaning in this context. Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on high performance Computers

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SSRN

SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

ReSLB: Load Balanced Workflow for Distributed Deep Learning Mass Spectrometry Database

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ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

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

作者： Li Mocheng Liu Yang Ou Yang Chen Zhiguang Xiao Nong Chen Tao Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences(Beijing) Beijing Institute of Lifeomics Beijing China

The proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. The parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. The heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. This paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. This work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that.

关键词： Deep learning Scalability Neural networks Data preprocessing Distributed databases Graphics processing units Clustering algorithms

Study on Deep Reinforcement Learning for Multi-Task Scheduling in Cloud Manufacturing

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SSRN

SSRN 2022年

作者： Xiao, Jiuhong Cai, Yishuai Wang, Wanru Chen, Yong College of Economic and Social Development Nankai University Tianjin300072 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Information Management and Artificial Intelligence Zhejiang University of Finance and Economics Hangzhou310018 China College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China

Cloud manufacturing is an emerging manufacturing paradigm, which enables the simultaneous processing of multiple manufacturing tasks submitted by customers through centralized management and planning of manufacturing services offered by distributed enterprises. To date, various meta-heuristics algorithms such as ant colony optimization algorithm, and genetic algorithm have been widely employed to solve the cloud manufacturing multi-task scheduling (CMMS) problem to improve the efficiency of multi-task scheduling. However, these algorithms need sophisticated design procedures and laborious parameter tuning, making their adaptation to dynamic, large-scale manufacturing environments difficult. Deep reinforcement learning (DRL), because of its efficiency and flexibility, has recently attracted increasing attention from researchers and has shown promising performance in solving combinatorial optimization problems. Nevertheless, little is known about the performance of DRL technologies in solving the CMMS problem. Therefore, in this study, three state-of-the-art DRL technologies, including proximal policy optimization (PPO), deep deterministic policy gradient, and normalized advantage function, are selected to systematically investigate the potential of DRL in solving the CMMS problem. A comprehensive comparison of these DRL technologies is conducted in terms of effectiveness and efficiency, robustness and adaptability, and scalability. Experimental results indicate that PPO is superior to other technologies in solving the CMMS problem. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation

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arXiv 2022年

作者： Zhang, Wenju Zhang, Xiang Liao, Qing Lan, Long Wang, Mengzhu Wang, Wei Peng, Baoyun Ding, Zhengming Institute for Quantum Information State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China Tulane University New OrleansLA United States

Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme. In this paper, we identify a new property termed equity, which indicates the balance degree of predicted classes, to demystify the efficacy of nuclear norm maximization for UDA theoretically. With this in mind, we offer a new discriminability-and-equity maximization paradigm built on squares loss, such that predictions are equalized explicitly. To verify its feasibility and flexibility, two new losses termed Class Weighted Squares Maximization (CWSM) and Normalized Squares Maximization (NSM), are proposed to maximize both predictive discriminability and equity, from the class level and the sample level, respectively. Importantly, we theoretically relate these two novel losses (i.e., CWSM and NSM) to the equity maximization under mild conditions, and empirically suggest the importance of the predictive equity in UDA. Moreover, it is very efficient to realize the equity constraints in both losses. Experiments of cross-domain image classification on three popular benchmark datasets show that both CWSM and NSM contribute to outperforming the corresponding counterparts. © 2022, CC BY.

关键词： Image classification

Is GN-z11 powered by a super-Eddington massive black hole?

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arXiv 2024年

作者： Bhatt, Maulik Gallerani, Simona Ferrara, Andrea Mazzucchelli, Chiara D'Odorico, Valentina Valentini, Milena Zana, Tommaso Farina, Emanuele Paolo Chakraborty, Srija Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI-56126 Italy Instituto de Estudios Astrofísicos Facultad de Ingeniería y Ciencias Universidad Diego Portales Avenida Ejercito Libertador 441 Santiago Chile INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 TriesteI-34131 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 5 Roma00185 Italy Gemini Observatory NSF's NOIRLab 670 N A'ohoku Place HiloHI96720 United States

Context. Observations of z ∼ 6 quasars powered by supermassive black holes (SMBHs;MBH ∼ 108−10 Mo) challenge our current understanding of early black hole (BH) formation and evolution. The advent of the James Webb Space Telescope (JWST) has enabled the study of massive BHs (MBHs;MBH ∼ 106−7 Mo) up to z ∼ 11, thus bridging the properties of z ∼ 6 quasars to their ancestors. Aims. The JWST spectroscopic observations of GN-z11, a well-known z = 10.6 star-forming galaxy, have been interpreted with the presence of a super-Eddington (Eddington ratio ≡ λEdd ∼ 5.5) accreting MBH. To test this hypothesis, we used a zoom-in cosmological simulation of galaxy formation and BH co-evolution. Methods. We first tested the simulation results against the observed probability distribution function (PDF) of λEdd found in z ∼ 6 quasars. Then, in the simulation we selected the BHs that satisfy the following criteria: (a) 10 BH > 106 Mo. Next, we applied the extreme value statistics to the PDF of λEdd resulting from the simulation. Results. We find that the probability of observing a z ∼ 10 − 11 MBH accreting with λEdd ∼ 5.5 in the volume surveyed by JWST is very low ( © 2024, CC BY.

关键词： Uncertainty analysis

Improving performance for simulating complex fluids on massively parallel computers by component loop-unrolling and communication hiding 22

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Improving performance for simulating complex fluids on massi...

22nd IEEE International Conference on high performance computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Guo, Xiao-Wei Li, Chao Li, Wei Cao, Yu Liu, Yi Zhao, Ran Zhang, Sen Yang, Canqun Institute for Quantum Information State Key Laboratory of High Performance Computing Colloege of Computer National University of Defense Technology Changsha Hunan China Colloege of Meteorology and Oceanology National University of Defense Technology Changsha Hunan China Press and Communication Center of the Pla Technical Support Office of Network Department Beijing China

ISBN: (纸本)9781728176499

Due to the complex geometry and physical models of real-world engineering applications, the parallel performance of the mainstream computational fluid dynamics(CFD) codes is unsatisfactory. For complex fluids, an extra stress tensor governed by constitutive equations including nine components brings much more amount of computations. This paper focused on optimizing the most compute-intensive part of a simulation for complex fluids: The iterative linear solver for solving multicomponent equations. Based on the most widely used opensource CFD code OpenFOAM, we unrolled the component loops and replaced the blocking collective MPI calls to non-blocking communications. After operation rescheduling between the loops, the collective communications could be partly overlapped by the computations. Taking the preconditioned conjugate gradient (PCG) algorithm for instance, we presented the complete loop unrolled algorithm for solving multi-component equations. The numerical experiments showed 8.0%29.0% simulation time reduction for a demonstrative case with 2 million cells on 642048 cores. It is worth noting that the approach proposed in this paper is a high-level scheduling algorithm and could be used in combination with other intra-component optimization algorithms, *** pipelined CG methods. © 2020 IEEE.

关键词： Constitutive equations