量子模拟中的超经典计算。

IF 45.8 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Science Pub Date : 2025-03-12 DOI:10.1126/science.ado6285
Andrew D. King, Alberto Nocera, Marek M. Rams, Jacek Dziarmaga, Roeland Wiersema, William Bernoudy, Jack Raymond, Nitin Kaushal, Niclas Heinsdorf, Richard Harris, Kelly Boothby, Fabio Altomare, Mohsen Asad, Andrew J. Berkley, Martin Boschnak, Kevin Chern, Holly Christiani, Samantha Cibere, Jake Connor, Martin H. Dehn, Rahul Deshpande, Sara Ejtemaee, Pau Farre, Kelsey Hamer, Emile Hoskinson, Shuiyuan Huang, Mark W. Johnson, Samuel Kortas, Eric Ladizinsky, Trevor Lanting, Tony Lai, Ryan Li, Allison J. R. MacDonald, Gaelen Marsden, Catherine C. McGeoch, Reza Molavi, Travis Oh, Richard Neufeld, Mana Norouzpour, Joel Pasvolsky, Patrick Poitras, Gabriel Poulin-Lamarre, Thomas Prescott, Mauricio Reis, Chris Rich, Mohammad Samani, Benjamin Sheldan, Anatoly Smirnov, Edward Sterpka, Berta Trullas Clavera, Nicholas Tsai, Mark Volkmann, Alexander M. Whiticar, Jed D. Whittaker, Warren Wilkinson, Jason Yao, T. J. Yi, Anders W. Sandvik, Gonzalo Alvarez, Roger G. Melko, Juan Carrasquilla, Marcel Franz, Mohammad H. Amin
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引用次数: 0

摘要

量子计算机有望解决传统计算机无法解决的某些问题。建立这种能力,特别是针对有影响和有意义的问题,仍然是一个核心挑战。在这里,我们表明超导量子退火处理器可以快速生成与Schrödinger方程解密切一致的样品。我们在二维、三维和无限维自旋玻璃的模型猝灭动力学中证明了纠缠的面积律标度,支持观察到的矩阵-积态方法的力的拉伸指数标度。我们表明,几种基于张量网络和神经网络的领先近似方法在合理的时间范围内无法达到与量子退火炉相同的精度。因此,量子退火炉可以回答经典计算无法解决的具有实际意义的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Beyond-classical computation in quantum simulation

Beyond-classical computation in quantum simulation
Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. However, establishing this capability, especially for impactful and meaningful problems, remains a central challenge. Here, we show that superconducting quantum annealing processors can rapidly generate samples in close agreement with solutions of the Schrödinger equation. We demonstrate area-law scaling of entanglement in the model quench dynamics of two-, three-, and infinite-dimensional spin glasses, supporting the observed stretched-exponential scaling of effort for matrix-product-state approaches. We show that several leading approximate methods based on tensor networks and neural networks cannot achieve the same accuracy as the quantum annealer within a reasonable time frame. Thus, quantum annealers can answer questions of practical importance that may remain out of reach for classical computation.
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来源期刊
Science
Science 综合性期刊-综合性期刊
CiteScore
61.10
自引率
0.90%
发文量
0
审稿时长
2.1 months
期刊介绍: Science is a leading outlet for scientific news, commentary, and cutting-edge research. Through its print and online incarnations, Science reaches an estimated worldwide readership of more than one million. Science’s authorship is global too, and its articles consistently rank among the world's most cited research. Science serves as a forum for discussion of important issues related to the advancement of science by publishing material on which a consensus has been reached as well as including the presentation of minority or conflicting points of view. Accordingly, all articles published in Science—including editorials, news and comment, and book reviews—are signed and reflect the individual views of the authors and not official points of view adopted by AAAS or the institutions with which the authors are affiliated. Science seeks to publish those papers that are most influential in their fields or across fields and that will significantly advance scientific understanding. Selected papers should present novel and broadly important data, syntheses, or concepts. They should merit recognition by the wider scientific community and general public provided by publication in Science, beyond that provided by specialty journals. Science welcomes submissions from all fields of science and from any source. The editors are committed to the prompt evaluation and publication of submitted papers while upholding high standards that support reproducibility of published research. Science is published weekly; selected papers are published online ahead of print.
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