August 2025

Journal

Beyond-classical computation in quantum simulation

By:
King, Andrew; Nocera, Alberto ; Rams, Marek; Dziarmaga, Jacek; Wiersema, Roeland; Bernoudy, William; Raymond, Jack; Kaushal, Nitin ; Heinsdorf, Niclas; Harris, Richard; Boothby, Kelly; Altomare, Fabio; Berkley, Andrew; Boschnak, Martin; Christiani, Holly; Cibere, Samantha; Connor, Jake; Dehn, Martin; Deshpande, Rahul; Ejtemaee, Sara; Hamer, Kelsey; Hoskinson, Emile; Huang, Shuiyuan; Johnson, Mark; Kortas, Samuel; Ladizinsky, Eric; Lanting, Trevor; Lai, Tony; Li, Ryan; Donald, Allison; Marsden, Gaelen; Molavi, Reza; Oh, Travis; Neufeld, Richard; Norouzpour, Mana; Pasvolsky, Joel; Poitras, Patrick; Lamarre, Gabriel; Prescott, Thomas; Reis, Mauricio; Rich, Chris; Samani, Mohammad; Sheldan, Benjamin; Smirnov, Anatoly; Sterpka, Eddie; Clavera, Berta; Tsai, Nicholas; Volkmann, Mark; Whiticar, Alexander; Whittaker, Jed; Wilkinson, Warren; Yao, Jason; Yi, T; Alvarez, Gonzalo ; Melko, Roger G; Carrasquilla, Juan; Franz, Marcel; Amin, Mohammad
Journal Name:
Science
Page Number:
199-204
Volume:
388
Issue Number:
6743
Publication Date:
August 22, 2025
View DOI Listing:
https://doi.org/10.1126/science.ado6285

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 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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