Beyond-classical computation in quantum simulation
Andrew King, Alberto Nocera, Marek M. Rams, Jacek Dziarmaga, Roeland Wiersema, William Bernoudy, Jack Raymond, Nitin Kaushal, Niclas Heinsdorf, R. Harris, Kelly Boothby, Fabio Altomare, Mohsen Asad, A. J. Berkley, Martin Boschnak, Kevin Chern, Holly Christiani, Samantha Cibere, J. N. L. Connor, Martin H. Dehn, Rahul Deshpande, Sara Ejtemaee, Pau Farré, Kelsey Hamer, Emile Hoskinson, Shuiyuan Huang, Mark W. Johnson, Samuel Kortas, E. Ladizinsky, T. Lanting, Tony Lai, Ryan Li, Allison MacDonald, G. Marsden, Catherine C. McGeoch, Reza Molavi, Travis Oh, R. B. Neufeld, Mana Norouzpour, Joel Pasvolsky, Patrick F. Poitras, Gabriel Poulin-Lamarre, Thomas P. Prescott, Maurício Sedrez dos Reis, Chris Rich, Mohammad Samani, Benjamin Sheldan, Anatoly Yu. Smirnov, Edward Sterpka, Berta Trullas Clavera, Nicholas Tsai, Mark H. Volkmann, Alexander Whiticar, Jed D. Whittaker, Warren Wilkinson, Jason Yao, T. J. Yi, Anders W. Sandvik, Gonzalo A. Álvarez, Roger G. Melko, Juan Carrasquilla, Marcel Franz, M. H. S. Amin
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.