The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Colloquium : Quantum annealing and analog quantum computation
Open accessAbstract
The recent success in quantum annealing, i.e., optimization of the cost or energy functions of complex systems utilizing quantum fluctuations is reviewed here. The concept is introduced in successive steps through studying the mapping of such computationally hard problems to classical spin-glass problems, quantum spin-glass problems arising with the introduction of quantum fluctuations, and the annealing behavior of the systems as these fluctuations are reduced slowly to zero. This provides a general framework for realizing analog quantum computation.
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?
In the citing paper
“Mean field [85], DFT [86, 87], or DMFT [88], finally, have other limitations, e.g., they are essentially designed for weakly-correlated systems.”
From Can one trust quantum simulators? (2012) · cites this paper as [88]
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
(1979) · Cited by 43,841
Quantum Computation and Quantum Information
Nielsen, M. A., Chuang, I., & Grover, L. K. (2002) · American Journal of Physics · doi:10.1119/1.1463744 · Cited by 21,895
Quantum computation and quantum information
Law, J. (2001) · ACM SIGSOFT Software Engineering Notes · doi:10.1145/505482.505499 · Cited by 20,450
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Spin glasses: Experimental facts, theoretical concepts, and open questions
Binder, K., & Young, A. P. (1986) · Reviews of Modern Physics · doi:10.1103/revmodphys.58.801 · Cited by 5,191
Topology of cosmic domains and strings
Kibble, T. W. B. (1976) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/9/8/029 · Cited by 4,626
Quantum Mechanics Helps in Searching for a Needle in a Haystack
Grover, L. K. (1997) · Physical Review Letters · doi:10.1103/physrevlett.79.325 · Cited by 4,306
Edwards, S. F., & Anderson, P. W. (1975) · Journal of Physics F: Metal Physics · doi:10.1088/0305-4608/5/5/017 · Cited by 3,991
Spin Glass Theory and Beyond: An Introduction to the Replica Method and Its Applications
Mezard, M., Parisi, G., & Virasoro, M. (1986) · World Scientific Lecture Notes in Physics · doi:10.1142/0271 · Cited by 3,653
Flagged first, then by citations
Research works printed but not linked: 8
- 2005, Quantum Annealing and Related Optimization Methods
- Fu, Y., 1986, J. Phys. A, 19, 1620
- Hartmann, A., 2002, Optimization in Physics
- Hatano, N., 2005, Quantum Annealing and Related Optimization Methods
- Inoue, J.-I., 2005, Quantum Annealing and Related Optimization Methods
- Mezard, M., 1987, Spin Glass Theory and Beyond
- Rieger, H., 2005, Quantum Annealing and Related Optimization Methods
- Suzuki, S., 2005, Quantum Annealing and Related Optimization Methods
Cited by
Sorted by citations
Items 601–698 of 698
Statistics of the number of defects after quantum annealing in a thermal environment
Suzuki, S., Oshiyama, H., & Shibata, N. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0411 · Cited by 1
Suzuki, S. (2010) · Lecture notes in physics · doi:10.1007/978-3-642-11470-0_5 · Cited by 2
A quantum diffusion networkPreprint
Kesidis, G. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0908.1597 · Cited by 1
Towards optical quantum information processing using Rydberg dark-state polaritons
Paredes-Barato, D. (2014) · Durham e-Theses (Durham University) · Cited by 1
Decomposition Models for Complex Scheduling Applications
Tran, T. T. (2017) · TSpace (University of Toronto) · Cited by 1
Multipartite entanglement in quantum phase transitionsPreprint
Gabbrielli, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10537 · 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
Open-system modeling of quantum annealing: theory and applicationsPreprint
Yip, K.-W. (2021) · arXiv (Cornell University) · doi:10.25549/usctheses-c89-443939 · Cited by 3
Infidelity Analysis of Digital Counter-Diabatic Driving in Two Qubit SystemPreprint
Lei, O. (2024) · Preprints.org · doi:10.20944/preprints202407.2130.v1 · Cited by 1
Hen, I., Rieffel, E. G., Minh, N., & Venturelli, D. (2014) · NASA Technical Reports Server (NASA)
Extracting the trustworthiest way to service provider in complex online social networksPreprint
Liu, L. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1307.1681
Saha Institute of Nuclear Physics — Its Past and the Present
Ghosh, A. (2014) · Asia Pacific Physics Newsletter · doi:10.1142/s2251158x14000344
Zhu, Z., Ochoa, A. J., & Katzgraber, H. G. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.97.174425
A foundation for quantum programming and its highly-parallel virtual execution
Vandriessche, Y. (2012)
Current Quantum Information Science and Technology
Pulkkis, G., & Grahn, K. J. (2014) · Advances in information quality and management · doi:10.4018/978-1-4666-5888-2.ch420
Solving Constraint Satisfaction Problems with Matrix Product States
Pelton, S. (2017) · Journal of International Crisis and Risk Communication Research
Miyazaki, R., & Ohzeki, M. (2018) · Physical Review A · doi:10.1103/physreva.98.053839
Context Mixing via Ground State SearchPreprint
Imafuku, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.04160
Annealing Approach to Quantum TomographyPreprint
Imafuku, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02443
An Ensemble Approach for Compressive Sensing with QuantumPreprint
Ayanzadeh, R., Halem, M., & Finin, T. (2020) · arXiv (Cornell University) · doi:10.13016/m2o2sr-qgvl
Adiabatic quantum computation with spin ensembles
Mohseni, N., Narożniak, M., Pyrkov, A. N., Ivannikov, V., Dowling, J. P., & Byrnes, T. (2020) · AIP conference proceedings · doi:10.1063/5.0011393
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
Quantum-inspired Search Method for Low-energy States of Classical Ising Hamiltonians
Ueda, H., Otsuka, Y., & Yunoki, S. (2022) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.91.044005
Darwinism of Quantum Computing
Phatak, N., Singh, S. P., & Shinde, H. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.18657507
Quantum Neural Networks and Quantum Intelligence
Altaĭsky, M. V., & Kaputkina, N. E. (2020) · Studies in Rhythm Engineering · doi:10.1007/978-981-15-7253-1_6
Property of Quantum Decoding for Sourlas Codes of Which Tuples Are Randomly Decimated
Lee, S., Iwata, K., & Mimura, K. (2020) · International Symposium on Information Theory and its Applications
Ichiki, A., & Ohzeki, M. (2026) · Journal of the Physical Society of Japan · doi:10.48550/arxiv.2107.01792
Atomic Quantum Wires in Ising-spin Chain Models
Kim, M., Kim, K., & Ahn, J. (2021) · Conference on Lasers and Electro-Optics · doi:10.1364/cleo_qels.2021.sth1d.5
Quantum Annealing: Research and Applications
Mohamed, M. (2021) · UWSpace (University of Waterloo)
Yan, B., & Sinitsyn, N. A. (2021) · arXiv (Cornell University)
Breakdown of the weak coupling limit in experimental quantum annealing.Preprint
Bando, Y., Yip, K.-W., Chen, H., Lidar, D. A., & Nishimori, H. (2021) · arXiv (Cornell University)
Effects of Graph Network Connections on The Efficiency of Quantum AnnealingPreprint
Ege, A., & Müstecaplıoğlu, Ö. E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.11411
(2016) · SciPost Physics · doi:10.21468/scipost.report.47
(2017) · SciPost Physics · doi:10.21468/scipost.report.88
(2016) · SciPost Physics · doi:10.21468/scipost.report.40
Quantum Computing and Quantum Communication
Pulkkis, G., & Grahn, K. J. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-2255-3.ch671
Ising Machines—Quantum-Inspired Machines for Combinatorial Optimization—
Goto, H. (2022) · The Brain & Neural Networks · doi:10.3902/jnns.29.174
Controlled quantized adiabatic transport in a superlattice Wannier-Stark ladder
Unanyan, R. G., Vitanov, N. V., & Fleischhauer, M. (2023) · Journal of Physics B Atomic Molecular and Optical Physics · doi:10.1088/1361-6455/acb11a
A quantum annealing approach to solve max-cover problem
Uyen, N. T., An, N. C., Trang, D. M., & Sang, T. X. (2022) · Vinh University Journal of Science · doi:10.56824/vujs.2022nt29
Quantum walker in presence of a moving detector
Molla, M. A., & Goswami, S. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.128775
Semi-quantum Computer on Semi-qubits
Kamalov, T. F., Evdokimov, N. V., Volkova, O., Hassan, M. K., & Kamalov, Y. T. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-36960-5_81
Localization with non-Hermitian off-diagonal disorder
Saha, A., & Rakshit, D. (2026) · Physica B Condensed Matter · doi:10.1016/j.physb.2026.418289
A Tour of Adiabatic Quantum Computing
Allgood, N. R. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.1002604
Effects of T-Factor on Quantum Annealing Algorithms for Integer Factoring Problem
Liu, Z., Zheng, S., Yan, X., Ping, P., & Wang, L. (2023) · Journal of quantum computing · doi:10.32604/jqc.2023.045572
Landscape approximation of low-energy solutions to binary optimization problems
Tan, B. Y. L., Gan, B. Y., Leykam, D., & Angelakis, D. G. (2024) · Physical Review A · doi:10.1103/physreva.109.012433
Rajak, A., & Chakrabarti, B. K. (2024) · Indian Journal of Physics · doi:10.1007/s12648-023-03041-y
Self-citationPhysics for secure and efficient societies
Barbato, F. C. T., Barthélemy, M., Beck, C., Biamonte, J., Cirac, J. I., Malz, D., Marino, A., Seskir, Z. C., & Ventura‐Traveset, J. (2024) · Institute of Physics eBooks · doi:10.1088/978-0-7503-6342-6ch7
Floquet chiral quantum walk in a quantum computer
Bark, C. B., Kim, Y., & Park, M. J. (2024) · Physical review. B./Physical review. B · doi:10.1103/physrevb.109.l201117
An Almost Deterministic Cooling by Measurements
Yan, J., & Jing, J. (2024) · Advanced Quantum Technologies · doi:10.1002/qute.202300435
Infidelity Analysis of Digital Counter-Diabatic Driving in Simple Two-Qubit System
Lei, O. (2024) · Entropy · doi:10.3390/e26100877 · Cited by 1
Chinnusamy, T. R., Raja, V. L., Gopalakrishnan, T., & Manikandan, R. (2024) · Research Square · doi:10.21203/rs.3.rs-5439496/v1
Optimization of conveyance of quantum particles by moving potential well
Morita, S., Teranishi, Y., & Miyashita, S. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043329
Ancilla-entangling Floquet kicks for accelerating quantum algorithms
Wang, C.-C. J., Lotshaw, P. C., Morris, T., Leyton‐Ortega, V., Claudino, D., & Humble, T. S. (2025) · Physical Review A · doi:10.1103/physreva.111.l010401
Resilience–runtime tradeoff relations for quantum algorithms
García-Pintos, L. P., O’Leary, T., Biswas, T., Bringewatt, J., Cincio, Ł., Brady, L. T., & Liu, Y.-K. (2025) · Reports on Progress in Physics · doi:10.1088/1361-6633/adac8b · Cited by 2
Quantum imaginary time evolution and the unit-disk maximum independent set problem
Penas, V. A., Losada, M., & Lamberti, P. W. (2025) · Physical Review A · doi:10.1103/physreva.111.042403
Optimization of Quantum Encoding for Combinatorial Problems in Rydberg Atom Array
Liu, J., Zhu, Y., Zheng, Q., Cui, J., & Su, Z. (2025) · International Conference on Quantum Communications, Networking, and Computing (QCNC) · doi:10.1109/qcnc64685.2025.00017
Improving adiabatic quantum factorization via chopped random-basis optimization
Yang, T., Xiong, M., Xue, M., Li, X., & Li, J. (2025) · Physical Review A · doi:10.1103/physreva.111.052617 · Cited by 2
Sequential spin-logic algorithmics for Ising machines
Michel, L., Rogier, M., & Lechenault, F. (2025) · Physical review. E · doi:10.1103/1y39-bgll
Hualei, 尹. Y., Xuyang, 卢. L., Qinghang, 张. Z., & Zengbing, 陈. C. (2025) · Laser & Optoelectronics Progress · doi:10.3788/lop250774
A Haskell Adiabatic DSL: Solving Classical Optimization Problems on Quantum Hardware
Li, L., Young, D., Graves, J. B., Dissanayake, C., & Sabry, A. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3747521
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
Mechanism of defect formation in the quantum annealing of the random transverse-field Ising chain
Juhász, R. (2025) · Physical review. B./Physical review. B · doi:10.1103/1nb9-9tbq
Xu, H., Wang, C., Zhang, Y., Zhang, Y., & Yuan, Z. (2025) · Physical Review Applied · doi:10.1103/dvxz-w9ts
Quantum Inspired Hazelnut Tree Search Algorithm
Das, A., Tekcham, R., Patton, K. E., & Marchang, N. (2025) · IEEE Guwahati Subsection Conference (GCON) · doi:10.1109/gcon65540.2025.11173289
Self-citationMitigating the barren plateau problem in linear optics
Horner, M. D. (2026) · Physical Review A · doi:10.1103/xndq-ttdj
Das, S., Biswas, S., Acharyya, M., & Chakrabarti, B. K. (2025) · Condensed Matter · doi:10.3390/condmat10040058
Self-citationAdaptive quantum optimization algorithms for programmable atom-cavity systems
Luo, Y., Li, X., & Lin, J. H. (2025) · Physical Review A · doi:10.1103/515w-htjp
McGarraghy, S., & Venkova, M. (2025) · Springer eBooks · doi:10.1007/978-3-319-07153-4_68-1
A Quantum Ising Model for Solving Sudoku Puzzles
Wang, W.-L., Tang, M.-H., Hussain, S., & Wang, K. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-08977-9_26
Learning parameter curves in feedback-based quantum algorithms
Perez, V. P., Grace, M., Arenz, C., & Magann, A. B. (2026) · Physical Review Research · doi:10.1103/pgbz-zm8h · Cited by 1
Quantum annealing optimization in the numerical solution of fractional PDEs by Sinc-Galerkin method
Shayegan, A. H. S. (2026) · International Journal of Quantum Information · doi:10.1142/s0219749926500048
Quantitative Analysis of the Effectiveness of Mid-anneal Measurement in Quantum Annealing
Takahashi, K., & Tanaka, S. (2026) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.95.034002 · Cited by 1
Variational quantum algorithm for constrained combinatorial optimization problems
Li, H.-M., Han, Y.-L., Wang, Z.-X., & Fei, S.-M. (2026) · Physical Review A · doi:10.1103/ykxd-h19w · Cited by 2
Tang, G.-X., Zhuang, J.-Z., Duan, L.-M., & Wu, Y. (2026) · Physical review. B./Physical review. B · doi:10.1103/v686-2c9f
An improved simulated bifurcation algorithm for hot rolling mill production scheduling
Shuang, F., Lu, Y., Gao, F., Wang, Y., & Lan, J. (2026) · International Journal of Production Research · doi:10.1080/00207543.2026.2649379
Accelerating optimisation algorithms via optical propagation in complex media
Delloye, L. (2024) · theses.fr (ABES) · doi:10.70675/4b413b60z53e5z41bdzb56ezd58ede1ef04d
Quantum bridge analytics: a QUBO approach to the uncapacitated facility location problem
Li, H., Hennig, R., Kochenberger, G. A., & Glover, F. (2026) · Annals of Operations Research · doi:10.1007/s10479-026-07201-1
Variational matrix product states for combinatorial optimization
Preisser, G., Keever, C. M., & Lubasch, M. (2026) · Physical Review Research · doi:10.1103/x7s3-ykb8
Quantum Enhanced Data-driven Learning for Defect Reduction in Etching Process
Yang, Z., Wang, L.-W., Chen, Y., Chao, Y.-H., Tsai, C., Wu, M.-T., Lee, H.-J., Lian, N.-T., Yang, T., Chen, K., et al. (2026) · ASMC proceedings · doi:10.1109/asmc69324.2026.11551187
Abe, T., Hino, K., & Tanaka, S. (2026) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.95.074003
Network-based prediction of drug combinations with quantum annealing
Ramos, D., Coutinho, B. C., & Magano, D. (2026) · Quantum Machine Intelligence · doi:10.1007/s42484-026-00411-7
Potencjał komputerów kwantowych w zarządzaniu ruchem lotniczym
Kaczorowski, P. (2026) · Journal of Konbin · doi:10.5604/01.3001.0055.8034
Optimal work fluctuations for thermally isolated systems in weakly driven processes
Nazé, P. (2026) · Physical review. E · doi:10.1103/skx4-8yvp
Beyond optimization: Harnessing quantum annealer dynamics for machine learning
Anonymous, Hayashi, A., MATSUMORI, T., Kaji, D., Kadowaki, T., & Nemoto, K. (2026) · Physical Review Research · doi:10.1103/dcvc-blqy · Cited by 1
Encoding computationally hard problems in triangular Rydberg atom arrays
Anonymous, Zhou, H.-H., Lu, Y.-M., & Liu, J.-G. (2026) · Physical Review A · doi:10.1103/kh98-chtn
Quantum-informed reduction algorithm for the maximum independent set problem
Schuetz, M. J. A., Yalovetzky, R., Andrist, R. S., Salton, G., Sun, Y., Raymond, R., Chakrabarti, S., Acharya, A., Shaydulin, R., Pistoia, M., et al. (2026) · Physical Review Research · doi:10.1103/nh3b-1wv5 · Cited by 3
A reduction scheme for general-order Ising-like Hamiltonians in quantum heuristic solvers
Mao, C., Mosharev, P. A., Wang, Y., & Yung, M. (2026) · Quantum Information Processing · doi:10.1007/s11128-026-05339-2
QUANG, T. N., Matsuyama, K., Shimizu, K., Sugano, H., Kurimoto, E., Suzuki, J., Chen, Z., Waidyasooriya, H. M., Kosei, N., Hariyama, M., et al. (2026) · IEEE Access · doi:10.1109/access.2026.3717906
A Quantum Annealing Approach for Solving Optimal Feature Selection and Next Release Problems
Zhang, Y., Wang, S., & Yang, W. (2026) · Entropy · doi:10.3390/e28080886
Wu, X., Wang, S., Wang, Y., & Li, Y. (2026) · Environmental Technology & Innovation · doi:10.1016/j.eti.2026.105203
Quantum dynamics shaping through fully actuated system representations
Ren, Z., Wang, L., Su, H., Kong, H., Shi, G., & Duan, G. (2026) · Quantum Science and Technology · doi:10.1088/2058-9565/aeae6b
A Gate-Based Quantum Ising Model Approach to Solving KenKen Puzzles Using Binary Encoding
Wang, W.-L., Tang, M.-H., Wang, K., Hussain, S., & Khan, S. U. R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-39763-8_3
Metadata
- Authors
- Das, A., & Chakrabarti, B. K.
- Affiliations
- Saha Institute of Nuclear Physics (IN)
- Venue
- Reviews of Modern Physics
- Publisher
- American Physical Society (APS)
- Published
- 2008-09-05
- Volume
- 80
- Issue
- 3
- Pages
- 1061-1081
- ISSN
- 0034-6861 (ISSN-L)
- 1539-0756
- OpenAlex
- W174492123
- Cited by
- 700
- References
- 93
- Citations by year
- 2026: 40 · 2025: 72 · 2024: 54 · 2023: 60 · 2022: 64 · 2021: 62 · 2020: 52 · 2019: 44 · 2018: 44 · 2017: 34 · 2016: 45 · 2015: 34 · 2014: 22 · 2013: 12 · 2012: 19
- FWCI
- 22.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Reviews of Modern Physics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- 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, quantum fluctuations, spin glass, quantum spin glass, combinatorial optimization, annealing behavior
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
