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Quantum Optimization of Fully Connected Spin Glasses
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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.
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“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]
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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,…”
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Items 101–200 of 209
Ferromagnetically Shifting the Power of Pausing
Izquierdo, Z. G., Grabbe, S. R., Hadfield, S., Marshall, J., Wang, Z., & Rieffel, E. G. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.15.044013 · Cited by 23
Error suppression in adiabatic quantum computing with qubit ensembles
Mohseni, N., Narożniak, M., Pyrkov, A. N., Ivannikov, V., Dowling, J. P., & Byrnes, T. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00405-2 · Cited by 18
Fair sampling of ground-state configurations of binary optimization problems
Zhu, Z., Ochoa, A. J., & Katzgraber, H. G. (2019) · Physical Review E · doi:10.1103/physreve.99.063314 · Cited by 17
Volpe, D., Cirillo, G. A., Zamboni, M., & Turvani, G. (2023) · IEEE Access · doi:10.1109/access.2023.3260765 · Cited by 18
Improving performance of logical qubits by parameter tuning and topology compensation
Raymond, J., Ndiaye, N., Rayaprolu, G., & King, A. D. (2020) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce49297.2020.00044 · Cited by 19
Mandrà, S., Guerreschi, G. G., & Aspuru‐Guzik, A. (2015) · Physical Review A · doi:10.1103/physreva.92.062320 · Cited by 17
Self-citationA Cost and Power Feasibility Analysis of Quantum Annealing for NextG Cellular Wireless Networks
Kasi, S., Warburton, P., Kaewell, J., & Jamieson, K. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3326469 · Cited by 20
Impacts of noise and structure on quantum information encoded in a quantum memory
Otten, M., Kapoor, K., Özgüler, A. B., Holland, E. T., Kowalkowski, J., Alexeev, Y., & Lyon, A. L. (2021) · Physical Review A · doi:10.1103/physreva.104.012605 · Cited by 19
Minimizing minor embedding energy: an application in quantum annealing
Fang, Y.-L., & Warburton, P. A. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-02681-x · Cited by 16
Breakdown of the Weak-Coupling Limit in Quantum Annealing
Bando, Y., Yip, K.-W., Chen, H., Lidar, D. A., & Nishimori, H. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.17.054033 · Cited by 16
Hamerly, R., Inagaki, T., McMahon, P. L., Venturelli, D., Marandi, A., Onodera, T., Ng, E., Langrock, C., Inaba, K., Honjo, T., et al. (2018) · arXiv (Cornell University) · Cited by 14
Self-citationLodewijks, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08043 · Cited by 15
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Bashar, M. K., Mallick, A., & Shukla, N. (2021) · IEEE Access · doi:10.1109/access.2021.3124808 · Cited by 16
Viewing vanilla quantum annealing through spin glasses
Katzgraber, H. G. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab6ba · Cited by 14
HyQSAT: A Hybrid Approach for 3-SAT Problems by Integrating Quantum Annealer with CDCL
Tan, S., Yu, M., Python, A., Shang, Y., Li, T., Lu, L., & Yin, J. (2023) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca56546.2023.10071022 · Cited by 14
Quadratic Unconstrained Binary Formulation for Traffic Signal Optimization on Real-World Maps
Shikanai, R., Ohzeki, M., & Tanaka, K. (2025) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.94.024001 · Cited by 26
Patch-planting spin-glass solution for benchmarking
Wang, W., Mandrà, S., & Katzgraber, H. G. (2017) · Physical review. E · doi:10.1103/physreve.96.023312 · Cited by 14
Self-citationFrom Ansätze to Z-Gates: A NASA View of Quantum Computing
Rieffel, E. G., Hadfield, S., Hogg, T., Mandrà, S., Marshall, J., Mossi, G., O’Gorman, B., Plamadeala, E., Tubman, N. M., Venturelli, D., et al. (2019) · Advances in parallel computing · doi:10.3233/apc190010 · Cited by 15
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Park, H., & Lee, H. (2022) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.91.074001 · Cited by 12
Neural Networks and Graph Algorithms with Next-Generation Processors
Hamilton, K. E., Schuman, C. D., Young, S. R., Imam, N., & Humble, T. S. (2018) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2018.00184 · Cited by 12
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Dong, Y., & Huang, Z. (2020) · International Journal of Theoretical Physics · doi:10.1007/s10773-020-04628-5 · Cited by 12
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Scalable connectivity for Ising machines: Dense to sparse
Sajeeb, M. M. H., Aadit, N. A., Chowdhury, S., Wu, T., Smith, C., Chinmay, D., Raut, A., Camsari, K. Y., Delacour, C., & Srimani, T. (2025) · Physical Review Applied · doi:10.1103/kx8m-5h3h · Cited by 11
Biclustering with a quantum annealer
Bottarelli, L., Bicego, M., Denitto, M., Pierro, A. D., Farinelli, A., & Mengoni, R. (2018) · Soft Computing · doi:10.1007/s00500-018-3034-z · Cited by 11
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Więckowski, A., Deffner, S., & Gardas, B. (2019) · Physical Review A · doi:10.1103/physreva.100.062304 · Cited by 11
Realization of Heisenberg models of spin systems with polar molecules in pendular states
Yue, W., Wei, Q., Kais, S., Friedrich, B., & Herschbach, D. R. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp00380e · Cited by 10
Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers
Volpe, D., Quetschlich, N., Graziano, M., Turvani, G., & Wille, R. (2024) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw62656.2024.00019 · Cited by 10
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Izquierdo, Z. G., Grabbe, S. R., Idris, H., Wang, Z., Marshall, J., & Rieffel, E. G. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.18.054056 · Cited by 12
4-Clique network minor embedding for quantum annealers
Pelofske, E. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.21.034023 · Cited by 11
How Small-World Interactions Can Lead to Improved Quantum Annealer Designs
Katzgraber, H. G., & Novotny, M. A. (2018) · Physical Review Applied · doi:10.1103/physrevapplied.10.054004 · Cited by 10
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Pinilla, J. P., & Wilton, S. J. E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20656-7_7 · Cited by 10
Pang, S. Y., Muniandy, S. V., & Kamali, M. Z. M. (2019) · International Journal of Theoretical Physics · doi:10.1007/s10773-019-04279-1 · Cited by 10
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Explorations of Quantum-Classical Approaches to Scheduling a Mars Lander Activity Problem
Tran, T. T., Wang, Z., Minh, N., Rieffel, E. G., Frank, J., O’Gorman, B., Venturelli, D., & Beck, J. C. (2016) · National Conference on Artificial Intelligence · Cited by 10
Self-citationCadeliña, A. R., Cuevas, M. A., Kallos, M., Montemayor, M. J., Uy, R. J., Rodriguez, J. B., & Tubola, O. D. (2024) · ECTI Transactions on Computer and Information Technology (ECTI-CIT) · doi:10.37936/ecti-cit.2024181.253577 · Cited by 8
Ramezanpour, A. (2018) · Physical Review A · doi:10.1103/physreva.98.062309 · Cited by 8
Quantum annealing speedup of embedded problems via suppression of Griffiths singularities
Knysh, S., Plamadeala, E., & Venturelli, D. (2020) · Physical review. B./Physical review. B · doi:10.1103/physrevb.102.220407 · Cited by 8
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Djidjev, H. N. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/acd13e · Cited by 7
Tennant, D., Dai, X., Martinez, A. J., Trappen, R., Melanson, D., Yurtalan, M. A., Tang, Y., Bedkihal, S., Yang, R., Novikov, S., et al. (2022) · npj Quantum Information · doi:10.1038/s41534-022-00590-8 · Cited by 6
Gomez-Tejedor, A., Osaba, E., & Villar-Rodríguez, E. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108481 · Cited by 8
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Brugger, J., Seidel, C., Streif, M., Wudarski, F., Dittel, C., & Buchleitner, A. (2022) · Physical Review A · doi:10.1103/physreva.105.042605 · Cited by 7
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Zhu, Z., Ochoa, A. J., & Katzgraber, H. G. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.97.174425
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Grant, E. (2020)
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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 ›
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