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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“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]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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.”
Method
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
Cited by
Sorted by citations
Items 201–234 of 234
Perturbed ferromagnetic chain: Tunable test of hardness in the transverse-field Ising model
O'Connor, D. T., Fry-Bouriaux, L., & Warburton, P. A. (2022) · Physical Review A · doi:10.1103/physreva.105.022410 · Cited by 1
“The distinct differences seen between classical and quantum evolutions therefore makes the PFC a useful gadget in differentiating dynamical systems, something which other gadgets cannot always exhibit [4, 26, 30].”
BackgroundMethodHu, X., Tatum, L. P., Almeida, S. F., Esatu, T. K., & King, T. (2022) · IEEE Electron Device Letters · doi:10.1109/led.2022.3224428 · Cited by 1
“As summarized in a recent review letter, there are many ways to implement an Ising machine [3], such as quantum annealers [4], [5], coherent Ising machines [6], [7],”
The Potential of Quantum Annealing for Rapid Solution Structure Identification [Slides]
Coffrin, C., & (NNSA), U. N. N. S. A. (2020) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/1599019 · Cited by 1
Author Correction: Analog errors in quantum annealing: doom and hope
Pearson, A., Mishra, A., Hen, I., & Lidar, D. A. (2020) · npj Quantum Information · doi:10.1038/s41534-020-00297-8 · Cited by 3
Self-citationOpen-system modeling of quantum annealing: theory and applicationsPreprint
Yip, K.-W. (2021) · arXiv (Cornell University) · doi:10.25549/usctheses-c89-443939 · Cited by 3
Variational quantum iterative power algorithms for global optimizationPreprint
Kyaw, T. H., Soley, M. B., Allen, B., Bergold, P., Sun, C., Batista, V. S., & Aspuru‐Guzik, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2208.10470 · Cited by 1
Scalable Guide Tree Construction Using Quantum Annealing for Multiple Sequence AlignmentPreprint
Youngjun, P., Kim, J., & Huh, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.30.626202 · Cited by 1
“Quantum annealers can obtain the approximate ground state of the Ising Hamiltonian with better scaling in time-to-solution than simulated annealing [63] and path-integral Monte Carlo [64].”
Kinematics variables of Higgs and background data
Zlokapa, A. (2020) · CaltechDATA · doi:10.22002/d1.1335 · Cited by 1
Neural Networks from a Physical Perspective
Sun, Y. (2019) · IEEE International Conference on Signal, Information and Data Processing (ICSIDP) · doi:10.1109/icsidp47821.2019.9173208
Dynamics of Quantum Annealers: Ising Model with Transverse Field Study
Więckowski, A., & Ptok, A. (2020) · Acta Physica Polonica A · doi:10.12693/aphyspola.138.686
Benchmarks and Controls for Optimization with Quantum Annealing
Grant, E. (2020)
A Quantum Annealing Approach to Reduce Covid-19 Spread on College CampusesPreprint
Sud, J., & Li, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.01220
“This research has shown proven quantum speedups for a specific graph optimization problem [25], as well as evidence of a scaling advantage of QA over classical simulated annealing for specific classes of more general problems [26], [27].”
Yoshizawa, A. (2023) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.14.152
Solving the Turbine Balancing Problem using Quantum Annealing
Unterauer, A., Bucher, D., Knoll, M., Economides, C., Lachner, M., Germain, T., Kessel, M., Hajdinovic, S., & Stein, J. (2024) · GECCO Companion · doi:10.1145/3638530.3664107
“An emerging technology that has shown the potential to solve optimization problems advantageously compared to known heuristics is quantum computing [Albash and Lidar 2018; Denchev et al. 2016; Ebadi et al. 2022].”
BackgroundConvergence Rate of Algorithm for Solving Linear Equations by Quantum Annealing
Tikhomirov, S. B., & Shalgin, V. S. (2024) · Computational Mathematics and Mathematical Physics · doi:10.1134/s0965542524700325
“The consistency of the Boltzmann distribution and the result produced by the D-Wave computer is quite well demonstrated in [13, 16–18] (for a discussion of the question of whether QA is superior to classical computers (e.g., see [19, 20]).”
Optimization of Player-Combinations in Multiplayer Games
Toda, K., Inoue, S., & Tobe, Y. (2024) · International Conference on Information Networking · doi:10.1109/icoin59985.2024.10572104
“Compared to classical annealing methods, quantum annealing has high parallelism and exponential efficiency [2], and is expected to be applied to large-scale optimization problems.”
MethodQuantum Optimization of Coherent Chaotic Systems: A Case for Buses of Kathmandu
Adhikari, K., Deppe, C., Ganeju, A., Lamichhane, I. K., Bhattarai, R., Limbu, M., & Bhattarai, N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82014-4_17
“However, upon closer examination, it becomes apparent that the speed-up for classical optimization problems is often quite modest[21, 22, 23].”
BackgroundRoshani, N., Stein, J., Zorn, M., Kölle, M., Altmann, P. J., & Linnhoff–Popien, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87327-0_12
Transforming machine learning: An exploration of quantum algorithms and their applications
Pawan, Y. V. R. N., & Kolla, B. P. (2025) · Advances in computers · doi:10.1016/bs.adcom.2025.06.005 · Cited by 2
Quantum speedup for solving the one-dimensional Hubbard model using quantum annealing
Vyas, K., Jin, F., Raedt, H. A. D., & Michielsen, K. F. L. (2026) · Physical Review Research · doi:10.1103/bj6c-t252
Piron, R., Ganzer, F., & Goursaud, C. (2025) · Conference on Computer Communications Workshops · doi:10.1109/infocomwkshps65812.2025.11152973
“Although the generic advantage of QA over classical methods remains an active field of research to-day [13], [14], it already outperformed its classical analogue, known as simulated annealing [15], to find the ground state of certain classes of Hamiltonians [11].”
Adaptive quantum optimization algorithms for programmable atom-cavity systems
Luo, Y., Li, X., & Lin, J. H. (2025) · Physical Review A · doi:10.1103/515w-htjp
“Prominent examples of such algorithms include QA and QAOA, both of which are promising candidates to provide significant quantum speedup over classical computing [11–14].”
Meller, M., Szeremi, V., & Brown, O. T. (2025) · Quantum · doi:10.22331/q-2025-12-02-1927 · Cited by 4
“This is the root of many controversies surrounding QA hardware and its prospective advantage over classical algorithms [48, 32, 49, 46].”
Computing a Sparse Approximate Inverse on Quantum Annealing Machines
Suresh, S., & Suresh, K. (2025) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4070578 · Cited by 1
Wang, A., Cai, D., Zhang, Y., Huang, Y., Feng, X., & Zhang, Z. (2025) · Physical Review Research · doi:10.1103/knt1-yd9s
“These methods have been shown to reach or approach the best-known solutions on benchmark QUBO instances and, in some cases, exhibit speed advantages over classical optimization techniques [29–34].”
Network separation modeling and quantum computing for developing wildfire fuelbreak strategy
Dent, S., Stoddard, K., Smith, M., Strelzoff, A., Cummings, C. L., Cegan, J. C., & Linkov, I. (2026) · Communications Engineering · doi:10.1038/s44172-026-00585-9
“In particular, compared to classical simulated annealing, quantum annealing has exhibited improved scalability for certain problem classes 25 , which is particularly important for our problem size.”
Performance evaluation and control improvements for solving optimization problems on NISQ platforms
Gilbert, V. (2024) · theses.fr (ABES) · doi:10.70675/81f9c638z5867z40e4zaed8ze9824ff5a163
Quantum-classical adaptive autoencoding of time-series signals for secure biometric authentication
Dutta, S., Basu, B., Roy, S., & Roy, U. (2026) · Quantum Machine Intelligence · doi:10.1007/s42484-026-00417-1
“Consistent with contemporary analyses of hybrid variational quantum learning (Albash and Lidar 2018; Gyongyosi and Imre 2020a ), the Tsn term dominates overall runtime under realistic hardware constraints.”
Digital payments in network formation games: Theory of adoption and quantum algorithms
Priazhkina, S., Palmer, S. J., Martín-Ramiro, P., Orús, R., Mugel, S., & Skavysh, V. (2026) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2026.105386
Nonlinear Model Predictive Control for Spacecraft Rendezvous via Quantum Optimization
Marchioli, V., Boggio, M., Volpe, D., Barbara, C., & Novara, C. (2026) · IEEE International Conference on Quantum Control, Computing and Learning (qCCL) · doi:10.1109/qccl70331.2026.11668577
“In particular, quantum annealers already provide practical platforms for solving large-scale combinatorial optimization tasks [5]–[14].”
A Scalable Compute-in-Memory Ising Machine for Combinatorial Optimization Problems
Huang, X., Qian, Y., Li, Y., Zhuo, C., & Yin, X. (2026) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano67986.2026.11667620
“For example, D-Wave quantum annealers require specialized operating conditions and provide limited connectivity [16], while optical Ising machines rely on complex photonic components that hinder large-scale integration [17].”
Esrafilian, V., Ravan, B. A., Javadi, A., & Daniali, H. (2026) · International Journal of Theoretical Physics · doi:10.1007/s10773-026-06497-w
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
