Quantum critical dynamics in a 5,000-qubit programmable spin glass
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The nonequilibrium quantum dynamics studied here are responsible for a scaling advantage over classical dynamics in optimization.
“The nonequilibrium quantum dynamics studied here are responsible for a scaling advantage over classical dynamics in optimization 25 .”
From Beyond-classical computation in quantum simulation (2025) · cites this paper as [25]
Reference 25 showed clearly distinct Kibble-Zurek exponents for QA, SQA, and SA in (dimerized) cubic problems.
“This was already done in Ref. 25 , which showed clearly distinct Kibble-Zurek exponents for QA, SQA, and SA in (dimerized) cubic problems.”
From Beyond-classical computation in quantum simulation (2025) · cites this paper as [25]
⟨q^2⟩ was found to be susceptible to distortion from post-critical dynamics between the quantum phase transition and the end of the quench.
“First, ⟨q 2 ⟩ was found 25 to be susceptible to distortion from post-critical dynamics between the QPT and the end of the quench, where the output state is read.”
From Beyond-classical computation in quantum simulation (2025) · cites this paper as [25]
QA was shown to have a dynamical advantage in optimization for these spin glasses.
“As a consequence, QA was shown to have a dynamical advantage in optimization for these spin glasses.”
From Beyond-classical computation in quantum simulation (2025) · cites this paper as [25]
The estimate of 1/ν = 1.55 (11) has recently been updated for the purposes of evaluating QA 25.
“The estimate of 1/ν = 1.55 (11) has recently been updated for the purposes of evaluating QA 25 .”
From Beyond-classical computation in quantum simulation (2025) · cites this paper as [25]
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Cites
Simulating physics with computers
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Programmable Quantum Simulations of Spin Systems with Trapped Ions
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What is the Computational Value of Finite-Range Tunneling?
Denchev, V. S., Boixo, S., Isakov, S. V., Ding, N., Babbush, R., Smelyanskiy, V., Martinis, J., & Neven, H. (2016) · Physical Review X · doi:10.1103/physrevx.6.031015 · Cited by 399 · see citing works
A quantum annealing architecture with all-to-all connectivity from local interactions
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Coherent quantum annealing in a programmable 2,000 qubit Ising chain
King, A. D., Suzuki, S., Raymond, J., Zucca, A., Lanting, T., Altomare, F., Berkley, A. J., Ejtemaee, S., Hoskinson, E., Huang, S., et al. (2022) · Nature Physics · doi:10.1038/s41567-022-01741-6 · Cited by 213 · see citing works
Self-citationNext-Generation Topology of D-Wave Quantum Processors
Boothby, K., Bunyk, P., Raymond, J., & Roy, A. (2020) · arXiv · doi:10.48550/arxiv.2003.00133 · Cited by 125 · see citing works
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Beyond-classical computation in quantum simulation
King, A. D., Nocera, A., Rams, M. M., Dziarmaga, J., Wiersema, R., Bernoudy, W., Raymond, J., Kaushal, N., Heinsdorf, N., Harris, R., et al. (2025) · Science · doi:10.1126/science.ado6285 · Cited by 138 · see citing works
Self-citationTransfer learning of optimal QAOA parameters in combinatorial optimization
Montañez-Barrera, J. A., Willsch, D., & Michielsen, K. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04743-4 · Cited by 19 · see citing works
Bias-field digitized counterdiabatic quantum algorithm for higher-order binary optimization
Romero, S. V., Visuri, A.-M., Cadavid, A. G., Simen, A., Solano, E., & Hegade, N. N. (2025) · Communications Physics · doi:10.1038/s42005-025-02270-3 · Cited by 18 · see citing works
Tutorial: calibration refinement in quantum annealing
Chern, K., Boothby, K., Raymond, J., Farré, P., & King, A. D. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1238988 · Cited by 11 · see citing works
Self-citationImproving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping
Maciejewski, F. B., Biamonte, J., Hadfield, S., & Venturelli, D. (2025) · Quantum · doi:10.22331/q-2025-11-06-1906 · Cited by 10 · see citing works
- Authors
- King, A. D., Raymond, J., Lanting, T., Harris, R., Zucca, A., Altomare, F., Berkley, A. J., Boothby, K., Ejtemaee, S., Enderud, C., Hoskinson, E., Huang, S., Ladizinsky, E., MacDonald, A. J. R., Marsden, G., Molavi, R., Oh, T., Poulin-Lamarre, G., Reis, M., Rich, C., Sato, Y., Tsai, N., Volkmann, M., Whittaker, J. D., Yao, J., Sandvik, A. W., & Amin, M. H.
- Affiliations
- D-Wave Systems (Canada) (CA)
- Boston University (US)
- Corresponding author
- King, Andrew D.
- Sandvik, Anders W.
- Amin, Mohammad H.
- ORCID
- Venue
- Nature
- Published
- 2023-04-19
- Volume
- 617
- Issue
- 7959
- Pages
- 61-66
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4366365204
- Cited by
- 242
- References
- 12
- Citations by year
- 2026: 42 · 2025: 97 · 2024: 70 · 2023: 31 · 2022: 1
- FWCI
- 22.62
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Keywords
- quantum annealing, spin glass, quantum critical dynamics, superconducting quantum processors, critical exponents, quantum optimization, energy minimization, quantum simulation, Monte Carlo comparison
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