加拿大西蒙弗雷泽大学Mohammad H. Amin团队研究了量子模拟中的超经典计算。2025年4月11日出版的《科学》杂志发表了这项成果。
量子计算机有望解决传统计算机无法解决的某些问题。然而，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。研究组证明了超导量子退火处理器可以快速生成与薛定谔方程解非常一致的样品。
他们证明了二维、三维和无限维自旋玻璃模型淬灭动力学中纠缠的面积律标度，支持了观察到的矩阵积态方法的拉伸指数标度。结果发现，基于张量网络和神经网络的几种领先的近似方法在合理的时间范围内无法达到与量子退火器相同的精度。因此，量子退火器可以回答经典计算可能无法解决的具有实际重要性的问题。
附：英文原文
Title: Beyond-classical computation in quantum simulation
Author: 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
Issue&Volume: 2025-04-11
Abstract: 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 Schrdinger 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.
DOI: ado6285
Source: https://www.science.org/doi/10.1126/science.ado6285