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Quantum Simulation for High-Energy Physics

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PRX Quantum 2023年第2期4卷 027001-027001页

作者： Christian W. Bauer Zohreh Davoudi A. Baha Balantekin Tanmoy Bhattacharya Marcela Carena Wibe A. de Jong Patrick Draper Aida El-Khadra Nate Gemelke Masanori Hanada Dmitri Kharzeev Henry Lamm Ying-Ying Li Junyu Liu Mikhail Lukin Yannick Meurice Christopher Monroe Benjamin Nachman Guido Pagano John Preskill Enrico Rinaldi Alessandro Roggero David I. Santiago Martin J. Savage Irfan Siddiqi George Siopsis David Van Zanten Nathan Wiebe Yukari Yamauchi Kübra Yeter-Aydeniz Silvia Zorzetti Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics Maryland Center for Fundamental Physics and the NSF Institute for Robust Quantum Simulation University of Maryland College Park Maryland 20742 USA Department of Physics University of Wisconsin Madison Wisconsin 53706 USA T-2 Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Fermi National Accelerator Laboratory Batavia Illinois 60510 USA Enrico Fermi Institute University of Chicago Chicago Illinois 60637 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Physics University of Chicago Chicago Illinois 60637 USA Department of Physics and Illinois Center for the Advanced Study of the Universe and Illinois Quantum Information Science and Technology Center University of Illinois Urbana Illinois 61801 USA QuEra Computing Inc Boston Massachusetts 02135 USA Department of Mathematics University of Surrey Guildford Surrey GU2 7XH United Kingdom Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University New York 11794-3800 USA Department of Physics Brookhaven National Laboratory Upton New York 11973 USA Peng Huanwu Center for Fundamental Theory Hefei Anhui 230026 China Interdisciplinary Center for Theoretical Study University of Science and Technology of China Hefei Anhui 230026 China Pritzker School of Molecular Engineering Chicago Quantum Exchange and Kadanoff Center for Theoretical Physics University of Chicago Illinois 60637 USA qBraid Co. Harper Court 5235 Chicago Illinois 60615 USA Department of Physics Harvard University Cambridge Massachusetts 02138 USA Department of Physics and Astronomy University of Iowa Iowa City Iowa 52242 USA Duke Quantum Center Duke University Durham North Carolina 27701 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Department of Physics Duke University

It is for the first time that quantum simulation for high-energy physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in quantum information sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This Roadmap is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond.

关键词： Quantum simulation

Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning

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arXiv 2018年

作者： Yuan, Weihao Stork, Johannes A. Kragic, Danica Wang, Michael Y. Hang, Kaiyu Department of Electronic and Computer Engineering Hong Kong University of Science and Technology and Hkust Robotics Institute Dept. of Mech. and Aerospace Engineering and the Department of Electronic and Computer Engineering Department of ComputerScience and Engineering and Hkust Institute for Advanced Study Perception and Learning Lab Centre for Autonomous Systems Kth Royal Institute of Technology Sweden

Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

High-performance Coatable Polarizer by Photoalignment

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High-performance Coatable Polarizer by Photoalignment

SID Symposium, Seminar, and Exhibition 2017, Display Week 2017

作者： Pan, Su Chigrinov, Vladimir G. Kwok, Hoi Sing State Key Lab on Advanced Displays and Optoelectronics Department of Electronic and Computer Engineering Hong Kong University of Science & Technology Hong Kong

ISBN: (纸本)9781510848757

A coatable polarizer with high performance is a perpetual goal because of its numerous advantages. Here, we demonstrate a photo-induced coatable polarizer with a dichroic ratio of more than 50, a contrast ratio of more than 300, and a thickness less than 500 nm. This polarizer offers a simple and economical technique to realize advanced displays with low cost, ultra-thin profile, flexible style, and high performance. © 2017 SID.

关键词： Azo dyes

Generating new classes of fixed-time stable systems with predefined upper bound for the settling time

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arXiv 2019年

作者： Aldana-López, Rodrigo Gómez-Gutiérrez, David Jiménez-Rodríguez, Esteban Sánchez-Torres, Juan Diego Defoort, Michael Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain Multi-Agent Autonomous Systems Lab Intel Labs Intel Tecnología de México Jalisco Mexico Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Jalisco Mexico Relativity6 Inc. Computer Science & Artificial Intelligence Lab. Jalisco Mexico Research Laboratory on Optimal Design Devices and Advanced Materials -OPTIMA- Department of Mathematics and Physics ITESO Jalisco Mexico LAMIH UMR CNRS 8201 INSA Polytechnic University of Hauts-de-France Valenciennes France

This paper aims to provide a methodology for generating autonomous and nonautonomous systems with a fixed-time stable equilibrium point where an Upper Bound of the Settling Time (UBST) is set a priori as a parameter of the system. In addition, some conditions for such an upper bound to be the least one are provided. This construction procedure is a relevant contribution when compared with traditional methodologies for generating fixed-time algorithms satisfying time constraints since current estimates of an UBST may be too conservative. The proposed methodology is based on time-scale transformations and Lyapunov analysis. It allows the presentation of a broad class of fixed-time stable systems with predefined UBST, placing them under a common framework with existing methods using time-varying gains. To illustrate the effectiveness of our approach, we generate novel, autonomous and non-autonomous, fixed-time stable algorithms with predefined least UBST. Copyright © 2019, The Authors. All rights reserved.

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

BLP-Boundary likelihood pinpointing networks for accurate temporal action localization

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arXiv 2018年

作者： Kong, Weijie Li, Nannan Liu, Shan Li, Thomas Li, Ge School of Electronic and Computer Engineering Peking University Shenzhen Peng Cheng Laboratory Shenzhen China Media Lab Tencent Shenzhen Advanced Institute of Information Technology Peking University Hangzhou China

Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries. Although most methods apply boundary regression paradigm to tackle this problem, we argue that the direct regression lacks detailed enough information to yield accurate temporal boundaries. In this paper, we propose a novel Boundary Likelihood Pinpointing (BLP) network to alleviate this deficiency of boundary regression and improve the localization accuracy. Given a loosely localized search interval that contains an action instance, BLP casts the problem of localizing temporal boundaries as that of assigning probabilities on each equally divided unit of this interval. These generated probabilities provide useful information regarding the boundary location of the action inside this search interval. Based on these probabilities, we introduce a boundary pinpointing paradigm to pinpoint the accurate boundaries under a simple probabilistic framework. Compared with other C3D feature based detectors, extensive experiments demonstrate that BLP significantly improves the localization performance of recent state-of-the-art detectors, and achieves competitive detection mAP on both THUMOS' 14 and ActivityNet datasets, particularly when the evaluation tloU is high. Copyright © 2018, The Authors. All rights reserved.

关键词： Regression analysis

Position Estimation Using PWM Excitation Method with Minimized Ripple Current

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Position Estimation Using PWM Excitation Method with Minimiz...

Sensorless Control for Electrical Drives (SLED)

作者： Ramakrishnan Raja Tomy Sebastian Mengqi Wang Mazharul Chowdhury School of Science Lanzhou University of Technology Lanzhou China Shenzhen Key Lab of Advanced Electron Device and Integration School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China

This paper introduces an optimum strategy for minimizing ripple current in position estimation using PWM based excitation at various operating ranges. The “optimum strategy” in this paper means strategy which provides a robust current derivative measurement for obtaining the saliency information while keeping the ripple current at minimum. The paper analyzes various PWM phase shifting strategies presently used for extracting position through the use of fundamental PWM excitation, in terms of robustness and ripple current. The paper also introduces new strategy for extracting robust current derivative measurement at various operating range with decreased ripple current, thereby improving the PWM Excitation based position estimation. Simulation and Experimental results shows the effectiveness of such strategy.

关键词： Switches Current measurement Observers Space vector pulse width modulation

Author Correction: Filling the gap between topological insulator nanomaterials and triboelectric nanogenerators

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Nature communications 2022年第1期13卷 1570页

作者： Mengjiao Li Hong-Wei Lu Shu-Wei Wang Rei-Ping Li Jiann-Yeu Chen Wen-Shuo Chuang Feng-Shou Yang Yen-Fu Lin Chih-Yen Chen Ying-Chih Lai Department of Materials Science and Engineering National Chung Hsing University Taichung 40227 Taiwan. Department of Physics National Chung Hsing University Taichung 40227 Taiwan. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA 90089 USA. Francis Bitter Magnet Lab Massachusetts Institute of Technology Cambridge MA 02139 USA. Department of Materials and Optoelectronic Science National Sun Yat-Sen University Kaohsiung 80424 Taiwan. i-Center for Advanced Science and Technology National Chung Hsing University Taichung 40227 Taiwan. Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung 40227 Taiwan. Department of Physics National Chung Hsing University Taichung 40227 Taiwan. yenfulin@nchu.edu.tw. i-Center for Advanced Science and Technology National Chung Hsing University Taichung 40227 Taiwan. yenfulin@nchu.edu.tw. Department of Materials and Optoelectronic Science National Sun Yat-Sen University Kaohsiung 80424 Taiwan. cychen@mail.nsysu.edu.tw. Department of Materials Science and Engineering National Chung Hsing University Taichung 40227 Taiwan. yclai@nchu.edu.tw. i-Center for Advanced Science and Technology National Chung Hsing University Taichung 40227 Taiwan. yclai@nchu.edu.tw. Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung 40227 Taiwan. yclai@nchu.edu.tw.

The Performance Investigation of Junctionless Transistor By Considering Different Recessed Gates

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The Performance Investigation of Junctionless Transistor By ...

IEEE Conference on Electron Devices and Solid-State Circuits

作者： Haijun Lou Wentao Li Yumei Yang Xinnan Lin School of Science Lanzhou University of Technology Lanzhou China Shenzhen Key Lab of Advanced Electron Device and Integration School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China

ISBN: (纸本)9781538662359

In this paper, the performance of junctionless transistor with three different recessed gates is investigated by numerical simulation. Then the impact of sidewall and the overlapped gate is discussed. The results show that the different recessed gates change the threshold voltage of devices. As compared between the three proposed devices, the gate-over structure has more superior subthreshold characterictics and drain current peformance. The sidewall angle variation in the gate-over devices is also studied. It will offer a helpful guide for fabricating the junctionless transistor with recessed gates.

关键词： Logic gates Transistors Threshold voltage Performance evaluation Semiconductor process modeling Numerical models