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arXiv 2024年

作者： Mohseni, Masoud Scherer, Artur Johnson, K. Grace Wertheim, Oded Otten, Matthew Aadit, Navid Anjum Bresniker, Kirk M. Camsari, Kerem Y. Chapman, Barbara Chatterjee, Soumitra Dagnew, Gebremedhin A. Esposito, Aniello Fahim, Farah Fiorentino, Marco Khalid, Abdullah Kong, Xiangzhou Kulchytskyy, Bohdan Li, Ruoyu Lott, P. Aaron Markov, Igor L. McDermott, Robert F. Pedretti, Giacomo Gajjar, Archit Silva, Allyson Sorebo, John Spentzouris, Panagiotis Steiner, Ziv Torosov, Boyan Venturelli, Davide Visser, Robert J. Webb, Zak Zhan, Xin Cohen, Yonatan Ronagh, Pooya Ho, Alan Beausoleil, Raymond G. Martinis, John M. Hewlett Packard Labs CA United States BC Canada Quantum Machines Israel Department of Physics University of Wisconsin–Madison WI United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States Hewlett Packard Enterprise TX United States Fermi National Accelerator Laboratory IL United States Applied Materials CA United States USRA Research Institute for Advanced Computer Science CA United States NASA Ames Research Center CA United States Synopsys CA United States Qolab CA United States Institute for Quantum Computing University of Waterloo ON Canada Department of Physics & Astronomy University of Waterloo ON Canada Perimeter Institute for Theoretical Physics ON Canada

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction procedures on a single logical qubit. Nevertheless, despite significant progress and excitement, the detailed path toward a full-stack scalable quantum computing technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation and learning/modeling quantum data generated by natural or engineered quantum systems. In order to estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. We show orders of magnitude enhancement in performance could be obtained by a combination of quantum hardware and algorithmic improvements. Furthermore, we argue that, to tackle today’s industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted p

关键词： Qubits

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The generative quantum eigensolver (GQE) and its application for ground state search

arXiv

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arXiv 2024年

作者： Nakaji, Kouhei Kristensen, Lasse Bjørn Campos-Gonzalez-Angulo, Jorge A. Vakili, Mohammad Ghazi Huang, Haozhe Bagherimehrab, Mohsen Gorgulla, Christoph Wong, FuTe McCaskey, Alex Kim, Jin-Sung Nguyen, Thien Rao, Pooja Aspuru-Guzik, Alan Chemical Physics Theory Group Department of Chemistry University of Toronto TorontoON Canada 1-1-1 Umezono Ibaraki Tsukuba Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama Japan Department of Computer Science University of Toronto TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Department of Physics Faculty of Arts and Sciences Harvard University CambridgeMA United States Department of Structural Biology St. Jude Children’s Research Hospital MemphisTN United States Institute of Medical Science University of Toronto TorontoON Canada NVIDIA Santa ClaraCA United States Department of Chemical Engineering & Applied Chemistry University of Toronto TorontoON Canada Department of Materials Science & Engineering University of Toronto TorontoON Canada Canadian Institute for Advanced Research TorontoON Canada

We introduce the generative quantum eigensolver (GQE), a novel method for applying classical generative models for quantum simulation. The GQE algorithm optimizes a classical generative model to produce quantum circuits with desired properties. Here, we develop a transformer-based implementation, which we name the generative pre-trained transformer-based (GPT) quantum eigensolver (GPT-QE), leveraging both pre-training on existing datasets and training without any prior knowledge. We demonstrate the effectiveness of training and pre-training GPT-QE in the search for ground states of electronic structure Hamiltonians. GQE strategies can extend beyond the problem of Hamiltonian simulation into other application areas of quantum computing. Copyright © 2024, The Authors. All rights reserved.

关键词： Ground state

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Deep Directly-Trained Spiking Neural Networks for Object Detection

arXiv

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arXiv 2023年

作者： Su, Qiaoyi Chou, Yuhong Hu, Yifan Li, Jianing Mei, Shijie Zhang, Ziyang Li, Guoqi School of Artificial Intelligence University of Chinese Academy of Sciences China College of Artificial Intelligence Xi'an Jiaotong University China Department of Precision Instrument Tsinghua University China School of Computer Science Peking University China School of Vehicle and Mobility Tsinghua University China Advanced Computing and Storage Lab Huawei Technologies Co Ltd. China Institute of Automation Chinese Academy of Sciences China

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown great success in achieving high performance on classification tasks with very few time steps. However, how to design a directly-trained SNN for the regression task of object detection still remains a challenging problem. To address this problem, we propose EMS-YOLO, a novel directly-trained SNN framework for object detection, which is the first trial to train a deep SNN with surrogate gradients for object detection rather than ANN-SNN conversion strategies. Specifically, we design a full-spike residual block, EMS-ResNet, which can effectively extend the depth of the directly-trained SNN with low power consumption. Furthermore, we theoretically analyze and prove the EMS-ResNet could avoid gradient vanishing or exploding. The results demonstrate that our approach outperforms the state-of-the-art ANN-SNN conversion methods (at least 500 time steps) in extremely fewer time steps (only 4 time steps). It is shown that our model could achieve comparable performance to the ANN with the same architecture while consuming 5.83× less energy on the frame-based COCO Dataset and the event-based Gen1 Dataset. Our code is available in https://***/BICLab/EMS-YOLO. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Application-level Benchmarking of Quantum computers using Nonlocal Game Strategies

arXiv

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arXiv 2023年

作者： Furches, Jim Chehade, Sarah Hamilton, Kathleen Wiebe, Nathan Marrero, Carlos Ortiz College of Computing Georgia Institute of Technology AtlantaGA30332 United States Physical Detection Systems and Deployment Division Pacific Northwest National Laboratory RichlandWA99354 United States Quantum Computational Science Group Oak Ridge National Laboratory Oak RidgeTN37830 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States

In a nonlocal game, two noncommunicating players cooperate to convince a referee that they possess a strategy that does not violate the rules of the game. Quantum strategies allow players to optimally win some games by performing joint measurements on a shared entangled state, but computing these strategies can be challenging. We present a variational quantum algorithm to compute quantum strategies for nonlocal games by encoding the rules of a nonlocal game into a Hamiltonian. We show how this algorithm can generate a short-depth optimal quantum strategy for a graph coloring game with a quantum advantage. This quantum strategy is then evaluated on fourteen different quantum hardware platforms to demonstrate its utility as a benchmark. Finally, we discuss potential sources of errors that can explain the observed decreased performance of the executed task and derive an expression for the number of samples required to accurately estimate the win rate in the presence of noise. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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Disentangling quantum neural networks for unified estimation of quantum entropies and distance measures

arXiv

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arXiv 2024年

作者： Shin, Myeongjin Lee, Seungwoo Lee, Junseo Lee, Mingyu Ji, Donghwa Yeo, Hyeonjun Jeong, Kabgyun School of Computing KAIST Daejeon34141 Korea Republic of Quantum AI Team Norma Inc. Seoul04799 Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of College of Liberal Studies Seoul National University Seoul08826 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Research Institute of Mathematics Seoul National University Seoul08826 Korea Republic of School of Computational Sciences Korea Institute for Advanced Study Seoul02455 Korea Republic of

The estimation of quantum entropies and distance measures, such as von Neumann entropy, Rényi entropy, Tsallis entropy, trace distance, and fidelity-induced distances such as the Bures distance, has been a key area of research in quantum information science. In our study, we introduce the disentangling quantum neural network (DEQNN), designed to efficiently estimate various physical quantities in quantum information. Estimation algorithms for these quantities are generally tied to the size of the Hilbert space of the quantum state to be estimated. Our proposed DEQNN offers a unified dimensionality reduction methodology that can significantly reduce the size of the Hilbert space while preserving the values of diverse physical quantities. We provide an in-depth discussion of the physical scenarios and limitations in which our algorithm is applicable, as well as the learnability of the proposed quantum neural network. Copyright © 2024, The Authors. All rights reserved.

关键词： Hilbert spaces

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Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

arXiv

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arXiv 2025年

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Computing and Digital Technology Birmingham City University BirminghamB55JU United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs’ activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://***/owenwang517/tvt-2025. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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Hybrid Recommendation System for Young Football Athletes Customized Training

Hybrid Recommendation System for Young Football Athletes Cu...

引用

Future of Information and Communication Conference, FICC 2020

作者： Matos, Paulo Rocha, João Gonçalves, Ramiro Santos, Filipe Abreu, David Soares, Hugo Martins, Constantino Computer Science Department Engineering Institute - Polytechnic of Porto Porto Portugal Trás-os-Montes e Alto Douro University Vila Real Portugal GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9783030394448

Information and communication technologies (ICTs) are been increasingly used in sports over the past decades, especially in professional football, with the goal of enhancing preparation and improve the athletes’ performance. Training programs, however, are not accessible for young and amateur athletes. Most of the systems available, don’t have learning skills to adjust, develop and find new suggestions for training, specifically designed for each athlete. In this paper we present the Smart Coach architecture and user adaptation model and describe our hybrid recommendation system to help the development of young athletes. It simplifies the relationship between the team’s technical staff leaders and their young athletes, enhancing the counselling of the young person and their development as an athlete. The system allows performance evaluation for young athletes utilizing various measurements. The match information is captured intuitively and adaptively by acquaintances, relatives and staff, using a comfortable smartphone interface. © Springer Nature Switzerland AG 2020.

关键词： Recommender systems

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Does Thermal Really Always Matter for RGB-T Salient Object Detection?

arXiv

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arXiv 2022年

作者： Cong, Runmin Zhang, Kepu Zhang, Chen Zheng, Feng Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong China The Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGBT SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Semantics

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PCKRF: Point Cloud Completion and Keypoint Refinement with Fusion Data for 6D Pose Estimation

arXiv

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arXiv 2022年

作者： Han, Yiheng Zhan, Irvin Haozhe Zeng, Long Wang, Yu-Ping Yi, Ran Yu, Minjing Lin, Matthieu Gaetan Sheng, Jenny Liu, Yong-Jin Faculty of Information Technology Beijing University of Technology Beijing China BNRist MOE-Key Laboratory of Pervasive Computing Department of Computer Science and Technology Tsinghua University Beijing China The Department of Advanced Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China The Beijing Institute of Technology Beijing China The Shanghai Jiao Tong University Shanghai China The College of Intelligence and Computing Tianjin University Tianjin China

Some robust point cloud registration approaches with controllable pose refinement magnitude, such as ICP and its variants, are commonly used to improve 6D pose estimation accuracy. However, the effectiveness of these methods gradually diminishes with the advancement of deep learning techniques and the enhancement of initial pose accuracy, primarily due to their lack of specific design for pose refinement. In this paper, we propose Point Cloud Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose refinement pipeline for 6D pose estimation. The pipeline consists of two steps. First, it completes the input point clouds via a novel pose-sensitive point completion network. The network uses both local and global features with pose information during point completion. Then, it registers the completed object point cloud with the corresponding target point cloud by our proposed Color supported Iterative KeyPoint (CIKP) method. The CIKP method introduces color information into registration and registers a point cloud around each keypoint to increase stability. The PCKRF pipeline can be integrated with existing popular 6D pose estimation methods, such as the full flow bidirectional fusion network, to further improve their pose estimation accuracy. Experiments demonstrate that our method exhibits superior stability compared to existing approaches when optimizing initial poses with relatively high precision. Notably, the results indicate that our method effectively complements most existing pose estimation techniques, leading to improved performance in most cases. Furthermore, our method achieves promising results even in challenging scenarios involving textureless and symmetrical objects. Our source code is available at https://***/zhanhz/KRF. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Performance Optimization for Intelligent Reflecting Surface Assisted Multicast MIMO Networks

Performance Optimization for Intelligent Reflecting Surface ...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Songling Zhang Zhaohui Yang Mingzhe Chen Danpu Liu Kai-Kit Wong H. Vincent Poor Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Zhejiang University Hangzhou China Zhejiang Lab Hangzhou China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Electronic and Electrical Engineering University College London London UK Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (纸本)9781665435413

In this paper, the problem of maximizing the sum rate of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 13.3 % gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

关键词： Manifolds Multicast algorithms Array signal processing Surface waves Simulation Performance gain Matrices

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