The DOI matches its Crossref record; 70 of the 71 references it cites were found in a registry or an index, and 1 are not checked yet.
Demonstration of a Scaling Advantage for a Quantum Annealer over Simulated Annealing
Open accessAbstract
Benchmarks of a quantum information processor establish, for the first time, an advantage for a quantum annealer over classical simulated annealing, an important milestone in the development of quantum optimizers and in the journey towards demonstrating a quantum speedup over classical computers.
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“A comparison with SA, spin-vector-monte-carlo [215], and simulated QA suggests that QA possesses the best scaling properties [7].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [7]
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“QA shows superior scaling properties compared to SA, where both are significantly inferior to simulated QA [7].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [7]
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Citations
Cites
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134 · cited 3×
“This advance allows us furthermore to demonstrate the first certifiable observation of an algorithmic scaling advantage obtained using quantum annealing hardware over an important general purpose classical algorithm, namely, over simulated annealing with single-spin updates (SA) [36].”
BackgroundMethodEquation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195 · cited 1×
“An update involves drawing a random angle ∈ ð0; 2π , and it is accepted according to the Metropolis-Hastings rule [85,86] with β 1⁄4 2.”
MethodAlgorithms for quantum computation: discrete logarithms and factoring
Shor, P. W. (2002) · Proceedings 35th Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365700 · Cited by 8,713 · cited 1×
“The holy grail is an exponential speedup, such as that believed to be attainable with Shor’s algorithm for factoring integers [1], or with the simulation of quantum systems [2–4].”
MethodQuantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
Simulating physics with computers
Feynman, R. P. (1982) · International Journal of Theoretical Physics · doi:10.1007/bf02650179 · Cited by 7,691
Lloyd, S. (1996) · Science · doi:10.1126/science.273.5278.1073 · Cited by 3,088 · cited 1×
“[3] Seth Lloyd, “Universal quantum simulators,” Science”
Collective Monte Carlo Updating for Spin Systems
Ulli Wolff, A. (1989) · Physical Review Letters · doi:10.1103/physrevlett.62.361 · Cited by 2,564 · cited 1×
“A sweep involves performing a singleWolff cluster update [87] along the imaginary-time direction for each spin.”
Probing many-body dynamics on a 51-atom quantum simulator
Bernien, H., Schwartz, S., Keesling, A., Levine, H., Omran, A., Pichler, H., Choi, S., Zibrov, A. S., Endres, M., Greiner, M., et al. (2017) · Nature · doi:10.1038/nature24622 · Cited by 2,460 · cited 1×
“[10] Hannes Bernien, Sylvain Schwartz, Alexander Keesling, Harry Levine, Ahmed Omran, Hannes Pichler, Soonwon Choi, Alexander S.”
BackgroundEdmonds, J. (1965) · Canadian Journal of Mathematics · doi:10.4153/cjm-1965-045-4 · Cited by 2,428 · cited 2×
“Here we check a polynomial-time algorithm for solving the minimumweight perfect-matching (MWPM) problem [70, 71] and show that it cannot be used to efficiently determine the ground state of the logical planted instances defined on Chimera with the gadget.”
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Flagged first, then by citations
Research works printed but not linked: 2
- P. W. Shor, Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 1994
- F. Hamze, 2004, UAI
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Sorted by citations
A quantum engineer's guide to superconducting qubits
Krantz, P., Kjærgaard, M., Yan, F., Orlando, T. P., Gustavsson, S., & Oliver, W. D. (2019) · Applied Physics Reviews · doi:10.1063/1.5089550 · Cited by 2,321
Quantum optimization using variational algorithms on near-term quantum devices
Moll, N., Barkoutsos, P., Bishop, L. S., Chow, J. M., Cross, A., Egger, D. J., Filipp, S., Fuhrer, A., Gambetta, J. M., Ganzhorn, M., et al. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab822 · Cited by 744
Perspectives of quantum annealing: methods and implementations
Hauke, P., Katzgraber, H. G., Lechner, W., Nishimori, H., & Oliver, W. D. (2020) · Reports on Progress in Physics · doi:10.1088/1361-6633/ab85b8 · Cited by 507
Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer
Aramon, M., Rosenberg, G., Valiante, E., Miyazawa, T., Tamura, H., & Katzgraber, H. G. (2019) · Frontiers in Physics · doi:10.3389/fphy.2019.00048 · Cited by 442
“With the same goal in mind, researchers have been inspired to push the envelope for such problems on novel hardware, such as the coherent Ising machine [32], as well as on graphics processing units (GPU) [27, 28] and application-specific CMOS hardware [34, 35].”
BackgroundHamerly, R., Inagaki, T., McMahon, P. L., Venturelli, D., Marandi, A., Onodera, T., Ng, E., Langrock, C., Inaba, K., Honjo, T., et al. (2019) · Science Advances · doi:10.1126/sciadv.aau0823 · Cited by 366
“It has been observed empirically on D-Wave quantum annealers that, for Chimera graph spin glasses, optimal time to solution scales as Tsoln o exp (O(N )) (18, 19, 49), while Tsoln at fixed anneal times increases more steeply (18).”
Background
Metadata
- Authors
- Albash, T., & Lidar, D. A.
- Affiliations
- University of Southern California (US)
- ORCID
- Albash, Tameem
- Venue
- Physical Review X
- Publisher
- American Physical Society (APS)
- Published
- 2018-07-19
- Volume
- 8
- Issue
- 3
- ISSN
- 2160-3308 (ISSN-L)
- OpenAlex
- W2992146840
- Cited by
- 238
- References
- 70
- Citations by year
- 2026: 13 · 2025: 46 · 2024: 33 · 2023: 28 · 2022: 28 · 2021: 28 · 2020: 33 · 2019: 24 · 2018: 4
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Physical Review X (publishedVersion, cc-by)
- arXiv (Cornell University) (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- APS March Meeting Abstracts
- Funders
- Army Research Office (W911NF-12-1-0523, W911NF-11-1-0268, W911NF-15-1-0582, W911NF-17-C-0050)
- National Science Foundation (INSPIRE-1551064)
- U.S. Department of Energy (DE-AC05-00OR22725)
- Office of the Director of National Intelligence
- Intelligence Advanced Research Projects Activity
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum annealing, simulated annealing, quantum advantage, benchmarking, combinatorial optimization, quantum annealers
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