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Colloquium : Quantum annealing and analog quantum computation
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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.
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“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]
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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
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
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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
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Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., & Aspuru-Guzik, A. (2012) · Scientific Reports · doi:10.1038/srep00571 · Cited by 366
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Harrington, P. M., Mueller, E. J., & Murch, K. W. (2022) · Nature Reviews Physics · doi:10.1038/s42254-022-00494-8 · Cited by 272
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Goto, H., Endo, K., Suzuki, M., Sakai, Y., Kanao, T., Hamakawa, Y., Hidaka, R., Yamasaki, M., & Tatsumura, K. (2021) · Science Advances · doi:10.1126/sciadv.abe7953 · Cited by 231
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Goto, H. (2016) · Scientific Reports · doi:10.1038/srep21686 · Cited by 241
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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
Quantum versus classical annealing of Ising spin glasses
Heim, B., Rønnow, T. F., Isakov, S. V., & Troyer, M. (2015) · Science · doi:10.1126/science.aaa4170 · Cited by 215
Solving a Higgs optimization problem with quantum annealing for machine learning
Mott, A., Job, J., Vlimant, J.-R., Lidar, D. A., & Spiropulu, M. (2017) · Nature · doi:10.1038/nature24047 · Cited by 210
Mapping of Ising models onto injection-locked laser systems
Utsunomiya, S., Takata, K., & Yamamoto, Y. (2011) · Optics Express · doi:10.1364/oe.19.018091 · Cited by 200
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Lienhard, V., de Léséleuc, S., Barredo, D., Lahaye, T., Browaeys, A., Schuler, M., Henry, L.-P., & Läuchli, A. M. (2018) · Physical Review X · doi:10.1103/physrevx.8.021070 · Cited by 209
Exotic freezing of response in a quantum many-body system
Das, A. (2010) · Physical Review B · doi:10.1103/physrevb.82.172402 · Cited by 163
Self-citationSuperadiabatic population transfer in a three-level superconducting circuit
Vepsäläinen, A. P., Danilin, S., & Paraoanu, G. S. (2019) · Science Advances · doi:10.1126/sciadv.aau5999 · Cited by 168
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Boothby, T., King, A. D., & Roy, A. (2015) · Quantum Information Processing · doi:10.1007/s11128-015-1150-6 · Cited by 171
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Decoherence in adiabatic quantum computation
Albash, T., & Lidar, D. A. (2015) · Physical Review A · doi:10.1103/physreva.91.062320 · Cited by 147
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Bando, Y., Susa, Y., Oshiyama, H., Shibata, N., Ohzeki, M., Gómez-Ruiz, F. J., Lidar, D. A., Suzuki, S., del Campo, A., & Nishimori, H. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033369 · Cited by 147
Katzgraber, H. G., Hamze, F., & Andrist, R. S. (2014) · Physical Review X · doi:10.1103/physrevx.4.021008 · Cited by 145
Prospects for quantum enhancement with diabatic quantum annealing
Crosson, E., & Lidar, D. A. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00313-6 · Cited by 153
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Quantum annealing with antiferromagnetic fluctuations
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Adiabatic perturbation theory and geometry of periodically-driven systems
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Consistency tests of classical and quantum models for a quantum annealer
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Quantum annealing of the graph coloring problem
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Probing the Bose glass–superfluid transition using quantum quenches of disorder
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Quantum annealing correction with minor embedding
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Sahimi, M., & Tahmasebi, P. (2021) · Physics Reports · doi:10.1016/j.physrep.2021.09.003 · Cited by 98
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Zero-temperature quantum annealing bottlenecks in the spin-glass phase
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Zhu, Z., Ochoa, A. J., Schnabel, S., Hamze, F., & Katzgraber, H. G. (2016) · Physical Review A · doi:10.1103/physreva.93.012317 · Cited by 74
Recent Progress in Quantum Simulation Using Superconducting Circuits
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Ground-state statistics from annealing algorithms: quantum versus classical approaches
Matsuda, Y., Nishimori, H., & Katzgraber, H. G. (2009) · New Journal of Physics · doi:10.1088/1367-2630/11/7/073021 · Cited by 71
Many-body transverse interactions in the quantum annealing of thep-spin ferromagnet
Seoane, B., & Nishimori, H. (2012) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8113/45/43/435301 · Cited by 75
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 ›
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