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.
| Original language | English |
|---|---|
| Pages (from-to) | 199-204 |
| Number of pages | 6 |
| Journal | Science |
| Volume | 388 |
| Issue number | 6743 |
| DOIs | |
| State | Published - Apr 11 2025 |
Funding
We are grateful to the technical and nontechnical staff at D-Wave, without whom this work would not have been possible. We thank L. Cincio, W. Zurek, V. Martín -Mayor, S. Boixo, A. Potter, and D. Huerga for insightful and helpful discussions. We also thank the ITensor community, especially M. Fishman and K. Pierce, for providing flexible and efficient tensor-network methods with support for GPU acceleration. Work at UBC was supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Alliance Quantum Program (grant ALLRP-578555), CIFAR and the Canada First Research Excellence Fund, Quantum Materials and Future Technologies Program. A.N. acknowledges the support of computational resources from the Advanced Research Computing at the University of British Columbia. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract DE-AC05-00OR22725. G.A. acknowledges support from the US Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Science Center. This research was also supported in part by the National Science Centre (NCN), Poland, under projects 2019/35/B/ST3/01028 (J.D.) and 2020/38/E/ST3/00150 (M.M.R.). R.G.M. was supported by NSERC. Research at Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Economic Development, Job Creation and Trade. J. Carrasquilla acknowledges support of NSERC, Compute Canada, and the Canadian Institute for Advanced Research (CIFAR) AI chair program. Resources used in preparing this research were provided in part by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute www.vectorinstitute.ai/partners. A.W.S. acknowledges support from the Simons Foundation (grant 511064). We are grateful to the technical and nontechnical staff at D-Wave, without whom this work would not have been possible. We thank Ł. Cincio, W. Zurek, V. Martín -Mayor, S. Boixo, A. Potter, and D. Huerga for insightful and helpful discussions. We also thank the ITensor community, especially M. Fishman and K. Pierce, for providing flexible and efficient tensor-network methods with support for GPU acceleration. Funding: Work at UBC was supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Alliance Quantum Program (grant ALLRP-578555), CIFAR and the Canada First Research Excellence Fund, Quantum Materials and Future Technologies Program. A.N. acknowledges the support of computational resources from the Advanced Research Computing at the University of British Columbia. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract DE-AC05-00OR22725. G.A. acknowledges support from the US Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Science Center. This research was also supported in part by the National Science Centre (NCN), Poland, under projects 2019/35/B/ST3/01028 (J.D.) and 2020/38/E/ ST3/00150 (M.M.R.). R.G.M. was supported by NSERC. Research at Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Economic Development, Job Creation and Trade. J. Carrasquilla acknowledges support of NSERC, Compute Canada, and the Canadian Institute for Advanced Research (CIFAR) AI chair program. Resources used in preparing this research were provided in part by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute www.vectorinstitute.ai/partners. A.W.S. acknowledges support from the Simons Foundation (grant 511064).