The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Quantum Optimization of Fully Connected Spin Glasses
Open accessAbstract
Quantum computing promises to be more efficient and significantly faster than today's classical computing. Researchers compare the performance of a quantum annealer with that of classical algorithms for the first time on hard spin-glass problems.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Since the number of random couplings scales as M 2 it is necessary to rescale the coupling strength to ensure extensive energy (correct scaling behavior) 71 ; we rescale couplers as J ij → Jij √ M/4 for every i in A and j in B.”
From Beyond-classical computation in quantum simulation (2025) · cites this paper as [71]
Experimental — accuracy not yet measured
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
Solvable Model of a Spin-Glass
Sherrington, D., & Kirkpatrick, S. (1975) · Physical Review Letters · doi:10.1103/physrevlett.35.1792 · Cited by 3,577
Collective Monte Carlo Updating for Spin Systems
Ulli Wolff, A. (1989) · Physical Review Letters · doi:10.1103/physrevlett.62.361 · Cited by 2,564
Ising formulations of many NP problems
Lucas, A. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00005 · Cited by 2,512
Quantum annealing in the transverse Ising model
Kadowaki, T., & Nishimori, H. (1998) · Physical Review E · doi:10.1103/physreve.58.5355 · Cited by 2,339
Exchange Monte Carlo Method and Application to Spin Glass Simulations
Hukushima, K., & Nemoto, K. (1995) · Journal of the Physical Society of Japan · doi:10.1143/jpsj.65.1604 · Cited by 2,223
Quantum annealing with manufactured spins
Johnson, M. W., Amin, M. H. S., Gildert, S., Lanting, T., Hamze, F., Dickson, N., Harris, R., Berkley, A. J., Johansson, J., Bunyk, P., et al. (2011) · Nature · doi:10.1038/nature10012 · Cited by 1,891
Exchange Monte Carlo method and application to spin glass simulationsPreprint
Hukushima, K., & Nemoto, K. (1996) · Cited by 1,645
Evidence for quantum annealing with more than one hundred qubits
Boixo, S., Rønnow, T. F., Isakov, S. V., Wang, Z., Wecker, D., Lidar, D. A., Martinis, J. M., & Troyer, M. (2014) · Nature Physics · doi:10.1038/nphys2900 · Cited by 810
Theory of Quantum Annealing of an Ising Spin Glass
Santoro, G. E., Martoňák, R., Tosatti, E., & Car, R. (2002) · Science · doi:10.1126/science.1068774 · Cited by 734
Defining and detecting quantum speedup
Rønnow, T. F., Wang, Z., Job, J., Boixo, S., Isakov, S. V., Wecker, D., Martinis, J. M., Lidar, D. A., & Troyer, M. (2014) · Science · doi:10.1126/science.1252319 · Cited by 601 · cited 5×
“For these reasons groups around the world have started to experimentally benchmark the machine [9–11], nurturing a lively discussion on whether the device is making functional use of quantum mechanics for computation [12] and how to properly measure speedups between different computational/experimental algorithms [13,…”
BackgroundMethodInfluentialQuantum Annealing of a Disordered Magnet
Brooke, J., Bitko, D., F., T., Rosenbaum, & Aeppli, G. (1999) · Science · doi:10.1126/science.284.5415.779 · Cited by 518
Minor-embedding in adiabatic quantum computation: I. The parameter setting problem
Choi, V. (2008) · Quantum Information Processing · doi:10.1007/s11128-008-0082-9 · Cited by 410
Search for a transition in the three-dimensional J Ising spin-glass
Bhatt, R. N., & Young, A. P. (1985) · Physical Review Letters · doi:10.1103/physrevlett.54.924 · Cited by 395
Mathematical foundation of quantum annealing
Morita, S., & Nishimori, H. (2008) · Journal of Mathematical Physics · doi:10.1063/1.2995837 · Cited by 381 · cited 1×
“While the founding principles of the technique have been investigated numerically [3], analytically [4,5], and experimentally [6] in the past decade, the disordered, interacting, time-dependent, and open nature of the many-body problem makes it very hard to draw universal conclusions about the power of the technique […”
BackgroundArchitectural Considerations in the Design of a Superconducting Quantum Annealing Processor
Bunyk, P. I., Hoskinson, E., Johnson, M. W., Tolkacheva, E., Altomare, F., Berkley, A. J., Harris, R., Hilton, J. P., Lanting, T., Przybysz, A. J., et al. (2014) · IEEE Transactions on Applied Superconductivity · doi:10.1109/tasc.2014.2318294 · Cited by 374
Finding low-energy conformations of lattice protein models by quantum annealing
Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., & Aspuru-Guzik, A. (2012) · Scientific Reports · doi:10.1038/srep00571 · Cited by 366
Application of statistical mechanics to NP-complete problems in combinatorial optimisation
Fu, Y., & Anderson, P. W. (1986) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/19/9/033 · Cited by 325
Cluster Monte Carlo algorithms
Wang, J., & Swendsen, R. H. (1990) · Physica A Statistical Mechanics and its Applications · doi:10.1016/0378-4371(90)90275-w · Cited by 280
Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor
Harris, R., Johnson, M. W., Lanting, T. M., Berkley, A. J., Johansson, J., Bunyk, P., Tolkacheva, E., Ladizinsky, E., Ladizinsky, N., Oh, T., et al. (2010) · Physical Review B · doi:10.1103/physrevb.82.024511 · Cited by 278
Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design
Choi, V. (2011) · Quantum Information Processing · doi:10.1007/s11128-010-0200-3 · Cited by 278
A case study in programming a quantum annealer for hard operational planning problems
Rieffel, E. G., Venturelli, D., O’Gorman, B., Do, M. B., Prystay, E. M., & Smelyanskiy, V. (2014) · Quantum Information Processing · doi:10.1007/s11128-014-0892-x · Cited by 221
Self-citationError-corrected quantum annealing with hundreds of qubits
Pudenz, K. L., Albash, T., & Lidar, D. A. (2014) · Nature Communications · doi:10.1038/ncomms4243 · Cited by 204
A scalable control system for a superconducting adiabatic quantum optimization processor
Johnson, M. W., Bunyk, P., Maibaum, F., Tolkacheva, E., Berkley, A. J., Chapple, E. M., Harris, R., Johansson, J., Lanting, T. M., Perminov, I., et al. (2010) · Superconductor Science and Technology · doi:10.1088/0953-2048/23/6/065004 · Cited by 165
Katzgraber, H. G., Hamze, F., & Andrist, R. S. (2014) · Physical Review X · doi:10.1103/physrevx.4.021008 · Cited by 145
Consistency tests of classical and quantum models for a quantum annealer
Albash, T., Vinci, W., Mishra, A., Warburton, P. A., & Lidar, D. A. (2015) · Physical Review A · doi:10.1103/physreva.91.042314 · Cited by 144
Quantum Computing and Quantum Bits in Mesoscopic Systems
Leggett, A. J., Ruggiero, B., & Silvestrini, P. (2004) · Springer eBooks · doi:10.1007/978-1-4419-9092-1 · Cited by 134
Experimental Determination of Ramsey Numbers
Bian, Z., Chudak, F. A., Macready, W. G., Clark, L., & Gaitan, F. (2013) · Physical Review Letters · doi:10.1103/physrevlett.111.130505 · Cited by 105
Adiabatic quantum programming: minor embedding with hard faults
Klymko, C., Sullivan, B. D., & Humble, T. S. (2014) · Quantum Information Processing · doi:10.1007/s11128-013-0683-9 · Cited by 99 · cited 1×
“Figure 1 shows an embedding of the SK model in a triangular portion [29,30] of the Vesuvius processor: each LB in the original problem of size N is represented by ðN=4Þ þ 1 qubits connected in a line.”
MethodBayesian network structure learning using quantum annealing
O’Gorman, B., Babbush, R., Perdomo‐Ortiz, A., Aspuru‐Guzik, A., & Smelyanskiy, V. (2015) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2015-02349-9 · Cited by 92
Self-citationMax 2-SAT with up to 108 qubits
Santra, S., Quiroz, G., Steeg, G. V., & Lidar, D. A. (2014) · New Journal of Physics · doi:10.1088/1367-2630/16/4/045006 · Cited by 61
Adiabatic quantum optimization with the wrong Hamiltonian
Young, K. C., Blume-Kohout, R., & Lidar, D. A. (2013) · Physical Review A · doi:10.1103/physreva.88.062314 · Cited by 60
Finding Large Clique Minors is Hard
Eppstein, D. (2009) · Journal of Graph Algorithms and Applications · doi:10.7155/jgaa.00183 · Cited by 27
Finite-size scaling analysis of the distributions of pseudo-critical temperatures in spin glasses
Billoire, A. H., Fernández, L. A., Maiorano, A., Marinari, E., Martín-Mayor, V., & Yllanes, D. (2011) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2011/10/p10019 · Cited by 22
How to test the “quantumness” of a quantum computer?
Zagoskin, A. M., Il’ichev, E. V., Grajcar, M., Betouras, J. J., & Nori, F. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00033 · Cited by 21
Proceedings of the ACM International Conference on Computing Frontiers
(2013) · doi:10.1145/2482767 · Cited by 21
Flagged first, then by citations
Cited by
Sorted by citations
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
Traffic Flow Optimization Using a Quantum Annealer
Neukart, F., Compostella, G., Seidel, C., Dollen, D. V., Yarkoni, S., & Parney, B. (2017) · Frontiers in ICT · doi:10.3389/fict.2017.00029 · Cited by 470
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
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
Self-citationQuantum annealing for industry applications: introduction and review
Yarkoni, S., Raponi, E., Bäck, T., & Schmitt, S. (2022) · Reports on Progress in Physics · doi:10.1088/1361-6633/ac8c54 · Cited by 341
Metadata
- Authors
- Venturelli, D., Mandrà, S., Knysh, S., O’Gorman, B., Biswas, R., & Smelyanskiy, V.
- Affiliations
- Ames Research Center (US)
- Google (United States) (US)
- Venue
- Physical Review X
- Publisher
- American Physical Society (APS)
- Published
- 2015-09-18
- Volume
- 5
- Issue
- 3
- ISSN
- 2160-3308 (ISSN-L)
- OpenAlex
- W1553502100
- Cited by
- 209
- References
- 35
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 19 · 2023: 15 · 2022: 14 · 2021: 19 · 2020: 19 · 2019: 23 · 2018: 15 · 2017: 18 · 2016: 21 · 2015: 19
- FWCI
- 23.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Physical Review X (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Aeronautics and Space Administration (NNX14AF62G, NNX12AK33A)
- Office of the Director of National Intelligence
- Intelligence Advanced Research Projects Activity
- AFRL Information Directorate (F4HBKC4162G001)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum annealing, spin glass, combinatorial optimization, quantum annealers, hard optimization problems
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
