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IEEE Conference on Decision and Control

作者： Malika Sader Wenyu Li Yanhui Yin Zhongmei Li Dexian Huang Zhongxin Liu Xiao He Chao Shang Department of Automation Tsinghua University Beijing China College of Artificial Intelligence Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China School of Automation Engineering North-East Electric Power University Jilin China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this article, we propose a data-driven solution to the output containment control problem of multiagent systems characterized by heterogeneous and unknown dynamics. The proposed data-driven scheme is hierarchical, which includes a network layer and a physical layer, bypassing the traditional modeling exercise and eliminating the need for explicit state-space models. In the network layer, a fully distributed observer without knowing the dynamics of leaders is designed to generate a containment trajectory. A data-driven approach based on data sampled from an auxiliary system is developed to overcome the dependence on the explicit state-space models for design of control gains. This paves the way for deriving a datadriven solution to the regulator equation, which is derived by employing the data informativity condition and relevant data in the physical layer. The results demonstrate that the closedloop system is asymptotically stable, and the regulated output containment converges to zero. Compared to generic modelbased hierarchical schemes, the assumptions that the system matrices are completely known and homogeneous on system dynamics can be relaxed.

关键词： Regulators Protocols System dynamics Distributed feedback devices Observers Physical layer Mathematical models State-space methods Trajectory Multi-agent systems

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Decentralized Stochastic Proximal Gradient Descent with Variance Reduction over Time-varying networks

arXiv

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arXiv 2021年

作者： Li, Xuanjie Xu, Yuedong Wang, Jessie Hui Wang, Xin Lui, John C.S. The School of Information Science and Technology Fudan University Shanghai 200237 China The Institute for Network Sciences and Cyberspace Tsinghua University Beijing100084 China The Beijing National Research Center for Information Science and Technology Beijing100084 China Department of Communication Science and Engineering Fudan University Shanghai 200433 China The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

In decentralized learning, a network of nodes cooperate to minimize an overall objective function that is usually the finite-sum of their local objectives, and incorporates a non-smooth regularization term for the better generalization ability. Decentralized stochastic proximal gradient (DSPG) method is commonly used to train this type of learning models, while the convergence rate is retarded by the variance of stochastic gradients. In this paper, we propose a novel algorithm, namely DPSVRG, to accelerate the decentralized training by leveraging the variance reduction technique. The basic idea is to introduce an estimator in each node, which tracks the local full gradient periodically, to correct the stochastic gradient at each iteration. By transforming our decentralized algorithm into a centralized inexact proximal gradient algorithm with variance reduction, and controlling the bounds of error sequences, we prove that DPSVRG converges at the rate of O(1/T) for general convex objectives plus a non-smooth term with T as the number of iterations, while DSPG converges at the rate O(√1/T ). Our experiments on different applications, network topologies and learning models demonstrate that DPSVRG converges much faster than DSPG, and the loss function of DPSVRG decreases smoothly along with the training *** Codes 68T05 Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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Speech Enhancement-assisted Voice Conversion in Noisy Environments

arXiv

引用

arXiv 2021年

作者： Chan, Yun-Ju Peng, Chiang-Jen Wang, Syu-Siang Wang, Hsin-Min Tsao, Yu Chi, Tai-Shih Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering Yuan Ze University Taoyuan Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan

Numerous voice conversion (VC) techniques have been proposed for the conversion of voices among different speakers. Although good quality of the converted speech can be observed when VC is applied in a clean environment, the quality degrades drastically when the system is run in noisy conditions. In order to address this issue, we propose a novel speech enhancement (SE)-assisted VC system that utilizes the SE techniques for signal pre-processing, where the VC and SE components are optimized in an joint training strategy with the aim to provide high-quality converted speech signals. We adopt a popular model, StarGAN, as the VC component and thus call the combined system as EStarGAN. We test the proposed EStarGAN system using a Mandarin speech corpus. The experimental results first verified the effectiveness of joint training strategy used in EStarGAN. Moreover, EStarGAN demonstrated performance robustness in various unseen noisy environments. The subjective listening test results further showed that EStarGAN can improve the sound quality of speech signals converted from noise-corrupted source utterances. Copyright © 2021, The Authors. All rights reserved.

关键词： Speech enhancement

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An Intelligent Edge-Deployable Indoor Air Quality Monitoring and Activity Recognition Approach

An Intelligent Edge-Deployable Indoor Air Quality Monitoring...

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International Conference on Signal Processing and information Security (ICSPIS)

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Abdelmalik Ouamane Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai United Arab Emirates Laboratory of LI3C University of Biskra Algeria Department of Computer Science University of Sharjah Sharjah United Arab Emirates

The surveillance of indoor air quality is paramount for ensuring environmental safety, a task made increasingly viable due to advancements in technology and the application of artificial intelligence and deep learning (DL) tools. This paper introduces an intelligent system dedicated to monitoring air quality and categorizing activities within indoor environments using a DL approach based on 1D Convolutional Neural networks (1D-CNNs). Our system integrates six diverse sensors to gather measurement parameters, which subsequently train a 1D CNN model for activity recognition. This proposed model boasts a lightweight and edge-deployable design, rendering it ideal for real-time applications. We conducted our experiments utilizing an air quality dataset specifically designed for Activity of Daily Living (ADL) classification. The results illustrate the proposed model’s efficacy, achieving a remarkable accuracy of 97.00%, a minimal loss value of 0.15%, and a swift prediction time of 41 milliseconds.

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Performance Analysis on RIS-Aided Wideband Massive MIMO OFDM Systems with Low-Resolution ADCs

arXiv

引用

arXiv 2024年

作者： Chen, Xianzhe Ren, Hong Pan, Cunhua Peng, Zhangjie Zhi, Kangda Liu, Yong Xi, Xiaojun Armada, Ana Garcia Wang, Cheng-Xiang The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China The Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai200234 China The School of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany The Wireless Technology Laboratory Huawei Technologies Co. Ltd. Shanghai201206 China The Department of Signal Theory and Communications University Carlos III of Madrid Leganes28911 Spain The National Mobile Communications Research Laboratory School Information of Science and Engineering Southeast University Nanjing210096 China The Pervasive Communication Research Center Purple Mountain Laboratories Nanjing211111 China

This paper investigates a reconfigurable intelligent surface (RIS)-aided wideband massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with low-resolution analog-to-digital converters (ADCs). Frequency-selective Rician fading channels are considered, and the OFDM data transmission process is presented in time domain. This paper derives the closed-form approximate expression of the uplink achievable rate, based on which the asymptotic system performance is analyzed when the number of the antennas at the base station and the number of reflecting elements at the RIS grow to infinity. Besides, the power scaling laws of the considered system are revealed to provide energy-saving insights. Furthermore, this paper proposes a gradient ascent-based algorithm to design the phase shifts of the RIS for maximizing the minimum user rate. Finally, numerical results are presented to verify the correctness of analytical conclusions and draw insights. Copyright © 2024, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI

arXiv

引用

arXiv 2024年

作者： Newlin, Nancy R. Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Chen, Jian Yang, Joseph Yuan-Mou Wu, Ye He, Yifei Zhang, Jiawei Zeng, Qingrun Zhang, Fan Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Ewert, Christian Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett A. Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’s Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Pediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA NashvilleTN United States Learning Research and Development Center University of Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathematics Eindhoven University of Technology Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Germany Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. Specifically, there is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Harmonized submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences. A machine learning approach that learned voxel-wise cross-acquisition relationships was the most effective at harmonizing connectomic, microst

关键词： Diffusion tensor imaging

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EdgeDis: Enabling Fast, Economical, and Reliable Data Dissemination for Mobile Edge Computing

arXiv

引用

arXiv 2023年

作者： Li, Bo He, Qiang Chen, Feifei Lyu, Lingjuan Bouguettaya, Athman Yang, Yun The College of Arts Business Law Education and Information Technology Victoria University MelbourneVIC3122 Australia The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia The School of Information Technology Deakin University Geelong Australia SONY AI Inc. Tokyo108-0075 Japan The School of Computer Science University of Sydney CamperdownNSW2006 Australia

Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis’s validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster;2) reduces dissemination costs by 48.21% to 91.87%;and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs. © 2023, CC BY-NC-SA.

关键词： Efficiency

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

引用

ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization 37

Multi-fidelity Bayesian Optimization with Max-value Entropy ...

引用

37th International Conference on Machine Learning, ICML 2020

作者： Takeno, Shion Fukuoka, Hitoshi Tsukada, Yuhki Koyama, Toshiyuki Shiga, Motoki Takeuchi, Ichiro Karasuyama, Masayuki Department of Computer Science Nagoya Institute of Technology Aichi Japan Center for Advanced Intelligence Project Riken Tokyo Japan Department of Materials Design Innovation Engineering Nagoya University Aichi Japan PRESTO Japan Science and Technology Agency Saitama Japan Department of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Center for Materials Research by Information Integration National Institute for Material Science Ibaraki Japan

ISBN: (纸本)9781713821120

In a standard setting of Bayesian optimization (BO), the objective function evaluation is assumed to be highly expensive. Multifidelity Bayesian optimization (MFBO) accelerates BO by incorporating lower fidelity observations available with a lower sampling cost. We propose a novel information-theoretic approach to MFBO, called multi-fidelity max-value entropy search (MF-MES), that enables us to obtain a more reliable evaluation of the information gain compared with existing information-based methods for MFBO. Further, we also propose a parallelization of MF-MES mainly for the asynchronous setting because queries typically occur asynchronously in MFBO due to a variety of sampling costs. We show that most of computations in our acquisition functions can be derived analytically, except for at most only two dimensional numerical integration that can be performed efficiently by simple approximations. We demonstrate effectiveness of our approach by using benchmark datasets and a real-world application to materials science data. © 2020 37th International Conference on Machine Learning, ICML 2020. All rights reserved.

关键词： Entropy

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Suppressing quantum errors by scaling a surface code logical qubit

arXiv

引用

arXiv 2022年

作者： Acharya, Rajeev Aleiner, Igor Allen, Richard Andersen, Trond I. Ansmann, Markus Arute, Frank Arya, Kunal Asfaw, Abraham Atalaya, Juan Babbush, Ryan Bacon, Dave Bardin, Joseph C. Basso, Joao Bengtsson, Andreas Boixo, Sergio Bortoli, Gina Bourassa, Alexandre Bovaird, Jenna Brill, Leon Broughton, Michael Buckley, Bob B. Buell, David A. Burger, Tim Burkett, Brian Bushnell, Nicholas Chen, Yu Chen, Zijun Chiaro, Ben Cogan, Josh Collins, Roberto Conner, Paul Courtney, William Crook, Alexander L. Curtin, Ben Debroy, Dripto M. Del Toro Barba, Alexander Demura, Sean Dunsworth, Andrew Eppens, Daniel Erickson, Catherine Faoro, Lara Farhi, Edward Fatemi, Reza Burgos, Leslie Flores Forati, Ebrahim Fowler, Austin G. Foxen, Brooks Giang, William Gidney, Craig Gilboa, Dar Giustina, Marissa Grajales Dau, Alejandro Gross, Jonathan A. Habegger, Steve Hamilton, Michael C. Harrigan, Matthew P. Harrington, Sean D. Higgott, Oscar Hilton, Jeremy Hoffmann, Markus Hong, Sabrina Huang, Trent Huff, Ashley Huggins, William J. Ioffe, Lev B. Isakov, Sergei V. Iveland, Justin Jeffrey, Evan Jiang, Zhang Jones, Cody Juhas, Pavol Kafri, Dvir Kechedzhi, Kostyantyn Kelly, Julian Khattar, Tanuj Khezri, Mostafa Kieferová, Mária Kim, Seon Kitaev, Alexei Klimov, Paul V. Klots, Andrey R. Korotkov, Alexander N. Kostritsa, Fedor Kreikebaum, John Mark Landhuis, David Laptev, Pavel Lau, Kim-Ming Laws, Lily Lee, Joonho Lee, Kenny Lester, Brian J. Lill, Alexander Liu, Wayne Locharla, Aditya Lucero, Erik Malone, Fionn D. Marshall, Jeffrey Martin, Orion McClean, Jarrod R. McCourt, Trevor McEwen, Matt Megrant, Anthony Meurer Costa, Bernardo Mi, Xiao Miao, Kevin C. Mohseni, Masoud Montazeri, Shirin Morvan, Alexis Mount, Emily Mruczkiewicz, Wojciech Naaman, Ofer Neeley, Matthew Neill, Charles Nersisyan, Ani Neven, Hartmut Newman, Michael Ng, Jiun How Nguyen, Anthony Nguyen, Murray Niu, Murphy Yuezhen O’Brien, Thomas E. Opremcak, Alex Platt, John Petukhov, Andre Potter, Rebecca Pryadko, Leonid Quintana, Chris Roushan, Pedram Rubin, Nicholas C. Saei, Negar Sank, Danie Google Research United States Department of Physics Columbia University New YorkNY United States Department of Electrical and Computer Engineering University of Massachusetts AmherstMA United States Department of Electrical and Computer Engineering Auburn University AuburnAL United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Department of Physics Institute for Quantum Information and Matter Walter Burke Institute for Theoretical Physics California Institute of Technology PasadenaCA United States Department of Electrical and Computer Engineering University of California RiversideCA United States USRA Research Institute for Advanced Computer Science Mountain ViewCA United States QuAIL NASA Ames Research Center Moffett Field CA United States Department of Physics University of California Santa BarbaraCA United States

Practical quantum computing will require error rates that are well below what is achievable with physical qubits. Quantum error correction [1, 2] offers a path to algorithmically-relevant error rates by encoding logical qubits within many physical qubits, where increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low in order for logical performance to improve with increasing code size. Here, we report the measurement of logical qubit performance scaling across multiple code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, both in terms of logical error probability over 25 cycles and logical error per cycle (2.914%±0.016% compared to 3.028%±0.023%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10−6 logical error per round floor set by a single high-energy event (1.6 × 10−7 when excluding this event). We are able to accurately model our experiment, and from this model we can extract error budgets that highlight the biggest challenges for future systems. These results mark the first experimental demonstration where quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation. © 2022, CC BY.

关键词： Qubits

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