Large-scale quantum reservoir learning with an analog quantum computer
Milan Kornjača, Hong-Ye Hu, Chen Zhao, Jonathan Wurtz, Phillip Weinberg, Majd Hamdan, Andrii Zhdanov, Sergio H. Cantu, Hengyun Zhou, Rodrigo Araiza Bravo, Kevin… 展开作者
Milan Kornjača, Hong-Ye Hu, Chen Zhao, Jonathan Wurtz, Phillip Weinberg, Majd Hamdan, Andrii Zhdanov, Sergio H. Cantu, Hengyun Zhou, Rodrigo Araiza Bravo, Kevin Bagnall, James I. Basham, Joseph Campo, Adam Choukri, Robert DeAngelo, Paige Frederick, David Haines, Julian Hammett, Ning Hsu, Ming-Guang Hu, Florian Huber, Paul Niklas Jepsen, Ningyuan Jia, Thomas Karolyshyn, Minho Kwon, John Long, Jonathan Lopatin, Alexander Lukin, Tommaso Macrì, Ognjen Marković, Luis A. Martínez-Martínez, Xianmei Meng, Evgeny Ostroumov, David Paquette, John Robinson, Pedro Sales Rodriguez, Anshuman Singh, Nandan Sinha, Henry Thoreen, Noel Wan, Daniel Waxman-Lenz, Tak Wong, Kai-Hsin Wu, Pedro L. S. Lopes, Yuval Boger, Nathan Gemelke, Takuya Kitagawa, Alexander Keesling, Xun Gao, Alexei Bylinskii, Susanne F. Yelin, Fangli Liu, Sheng-Tao Wang
Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks.
A Single Atom in Front of a Mirror is a Universal Reservoir Computer
Wyant College of Optical Sciences, University of Arizona（亚利桑那大学怀恩特光学科学学院）；Department of Physics, University of Arizona（亚利桑那大学物理系）；School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气工程与电信学院）
Analog neutral-atom for in-memory processing in quantum reservoir computing
Università degli Studi di Milano（米兰大学）；Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC（跨学科物理与复杂系统研究所（IFISC）巴利阿里群岛大学-西班牙国家研究委员会）；Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche（意大利国家研究委员会光子学与纳米技术研究所）
Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics
Imperial College London（伦敦帝国理工学院）；City St George’s, University of London（伦敦大学城市圣乔治学院）；The Alan Turing Institute（艾伦·图灵研究所）；Politecnico di Torino（都灵理工大学）
Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学）；DLR, Institute for Satellite Geodesy and Inertial Sensing（德国航空航天中心卫星大地测量与惯性传感研究所）；University of Bremen（不来梅大学）；Institut FEMTO-ST, Université Franche-Comté CNRS UMR 6174（弗朗什-孔泰大学CNRS FEMTO-ST研究所）
Leveraging Metrologically Useful States in Quantum Reservoir Networks
Naval Surface Warfare Center Panama City Division（海军水面武器中心巴拿马城分部）；The Johns Hopkins Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）；Qodex Quantum（Qodex量子）；University of South Carolina - Columbia（南卡罗来纳大学哥伦比亚分校）
A hidden bottleneck in classical and quantum linear reservoir computing
University of Turku（图尔库大学）；Turku Collegium for Science, Medicine and Technology, University of Turku（图尔库大学科学、医学与技术学院）；ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology（巴塞罗那科技研究所光子学研究所）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；École Normale Supérieure, PSL University（巴黎高等师范学院，巴黎文理研究大学）
Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective
Artificial Intelligence and Cyber Futures Institute, Charles Sturt University（查尔斯斯特大学人工智能与网络未来学院）；Cyber Futures Institute, Charles Sturt University（查尔斯斯特大学网络未来学院）
