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 201–300 of 698
Next Generation Power System Planning and Operation With Quantum Computation
Ganeshamurthy, P. A., Ghosh, K., O’Meara, C., Cortiana, G., Schiefelbein, J., & Monti, A. (2024) · IEEE Access · doi:10.1109/access.2024.3509743 · Cited by 35
Quantum annealing speedup over simulated annealing on random Ising chains
Zanca, T., & Santoro, G. E. (2016) · Physical review. B./Physical review. B · doi:10.1103/physrevb.93.224431 · Cited by 26
Adiabatic Quantum Search in Open Systems
Wild, D. S., Gopalakrishnan, S., Knap, M., Yao, N. Y., & Lukin, M. D. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.150501 · Cited by 28
Optimally Stopped Optimization
Vinci, W., & Lidar, D. A. (2016) · Physical Review Applied · doi:10.1103/physrevapplied.6.054016 · Cited by 27
Abah, O., Chiara, G. D., Paternostro, M., & Puebla, R. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.l022017 · Cited by 26
Quantum annealing and computation: challenges and perspectives
Chakrabarti, B. K., Leschke, H., Ray, P., Shirai, T., & Tanaka, S. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0419 · Cited by 26
Self-citationMallick, A., Zhao, Z., Bashar, M. K., Alam, S., Islam, M. M., Xiao, Y., Xu, Y., Aziz, A., Narayanan, V., Ni, K., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-28217-8 · Cited by 28
Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer
Ding, Y., Gonzalez-Conde, J., Lamata, L., Martín‐Guerrero, J. D., Lizaso, E., Mugel, S., Chen, X., Orús, R., Solano, E., & Sanz, M. (2023) · Entropy · doi:10.3390/e25020323 · Cited by 29
Quantum Computing in Telecommunication—A Survey
Phillipson, F. (2023) · Mathematics · doi:10.3390/math11153423 · Cited by 33
Advantages of Unfair Quantum Ground-State Sampling
Zhang, B. H., Wagenbreth, G., Martín-Mayor, V., & Hen, I. (2017) · Scientific Reports · doi:10.1038/s41598-017-01096-6 · Cited by 25
Vinci, W., & Lidar, D. A. (2018) · Physical Review A · doi:10.1103/physreva.97.022308 · Cited by 28
Financial risk management on a neutral atom quantum processor
Leclerc, L., Ortiz-Gutiérrez, L., Grijalva, S., Albrecht, B., Cline, J., Elfving, V. E., Signoles, A., Henriet, L., Bimbo, G. D., Sheikh, U. A., et al. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.043117 · Cited by 42
In‐Network Quantum Computing for Future 6G Networks
Urgelles, H., Maheshwari, S., Nande, S. S., Bassoli, R., Fitzek, F. H. P., & Monserrat, J. F. (2024) · Advanced Quantum Technologies · doi:10.1002/qute.202300334 · Cited by 29
A Spin-Optical Quantum Computing Architecture
de Gliniasty, G., Hilaire, P., Emeriau, P.-E., Wein, S. C., Salavrakos, A., & Mansfield, S. (2024) · Quantum · doi:10.22331/q-2024-07-24-1423 · Cited by 36
Wang, L., Tao, W. J., Li, N., Chi, S., Jiang, C., & Fang, S.-Y. (2018) · Environmental Earth Sciences · doi:10.1007/s12665-018-7927-z · Cited by 25
Energy Gap at First-Order Quantum Phase Transitions: An Anomalous Case
Tsuda, J., Yamanaka, Y., & Nishimori, H. (2013) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.82.114004 · Cited by 23
Application of Pontryagin's minimum principle to Grover's quantum search problem
Lin, C., Wang, Y., Kolesov, G., & Kalabic, U. V. (2019) · Physical Review A · doi:10.1103/physreva.100.022327 · Cited by 22
Analytical solution for nonadiabatic quantum annealing to arbitrary Ising spin Hamiltonian
Yan, B., & Sinitsyn, N. A. (2022) · Nature Communications · doi:10.1038/s41467-022-29887-0 · Cited by 23
Algorithms for Quantum Computers
Smith, J., & Mosca, M. (2012) · Springer eBooks · doi:10.1007/978-3-540-92910-9_43 · Cited by 24
Simulations of Shor’s algorithm using matrix product states
Wang, D. S., Hill, C. D., & Hollenberg, L. C. L. (2017) · Quantum Information Processing · doi:10.1007/s11128-017-1587-x · Cited by 22
Parameter Tuning Patterns for Random Graph Coloring with Quantum Annealing
Titiloye, O., & Crispin, A. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050060 · Cited by 22
Realizing exactly solvable SU(N) magnets with thermal atoms
Beverland, M. E., Alagic, G., Martin, M. J., Koller, A., Rey, A. M., & Gorshkov, A. V. (2016) · Physical Review A · doi:10.1103/physreva.93.051601 · Cited by 23
Ohzeki, M. (2017) · Scientific Reports · doi:10.1038/srep41186 · Cited by 23
Quantum trajectories for time-dependent adiabatic master equations
Yip, K.-W., Albash, T., & Lidar, D. A. (2018) · Physical Review A · doi:10.1103/physreva.97.022116 · Cited by 22
The promise of analog computation
MacLennan, B. J. (2014) · International Journal of General Systems · doi:10.1080/03081079.2014.920997 · Cited by 23
Quantum thermodynamics and quantum coherence engines
Tuncer, A., & Müstecaplıoğlu, Ö. E. (2020) · TURKISH JOURNAL OF PHYSICS · doi:10.3906/fiz-2009-12 · Cited by 26
Assessment of Quantum Annealing for the Construction of Satisfiability Filters
Azinović, M., Herr, D., Heim, B., Brown, E., & Troyer, M. (2017) · SciPost Physics · doi:10.21468/scipostphys.2.2.013 · Cited by 22
Metastable states in the frustrated triangular compounds Ca3Co2−xMnxO6 and Ca3Co2O6
Kim, J., Mun, E., Ding, X. X., Hansen, A., Jaime, M., Harrison, N., Yi, H. T., Chai, Y., Sun, Y., Cheong, S., et al. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.98.024407 · Cited by 22
Mehta, P., Cui, W., Wang, C.-H., & Marsland, R. (2019) · Physical review. E · doi:10.1103/physreve.99.052111 · Cited by 22
Multi-qubit correction for quantum annealers
Ayanzadeh, R., Dorband, J. E., Halem, M., & Finin, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-95482-w · Cited by 24
Forecasting timelines of quantum computingPreprint
Sevilla, J., & Riedel, C. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05045 · Cited by 62
Dynamic optimization based on quantum computation-A comprehensive review
Kou, H., Zhang, Y., & Lee, H. P. (2023) · Computers & Structures · doi:10.1016/j.compstruc.2023.107255 · Cited by 21
Counterdiabatic Driving for Periodically Driven Systems
Schindler, P. M., & Bukov, M. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.123402 · Cited by 21
Topological and dynamical properties of a generalized cluster model in one dimension
Ohta, T., Tanaka, S., Danshita, I., & Totsuka, K. (2016) · Physical review. B./Physical review. B · doi:10.1103/physrevb.93.165423 · Cited by 21
Nested quantum annealing correction at finite temperature: p -spin models
Matsuura, S., Nishimori, H., Vinci, W., & Lidar, D. A. (2019) · Physical Review A · doi:10.1103/physreva.99.062307 · Cited by 21
Solving Set Cover with Pairs Problem using Quantum Annealing
Cao, Y., Jiang, S., Perouli, D., & Kais, S. (2016) · Scientific Reports · doi:10.1038/srep33957 · Cited by 21
Quantum Science and Quantum Technology
Wang, Y., & Song, X. (2020) · Statistical Science · doi:10.1214/19-sts745 · Cited by 22
Dominant Reaction Pathways by Quantum Computing
Hauke, P., Mattiotti, G., & Faccioli, P. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.028104 · Cited by 20
The Potential of Quantum Computing for Geoscience
Sahimi, M., & Tahmasebi, P. (2022) · Transport in Porous Media · doi:10.1007/s11242-022-01855-8 · Cited by 22
Quantum dropout: On and over the hardness of quantum approximate optimization algorithm
Wang, Z., Zheng, P.-L., Wu, B., & Zhang, Y. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.023171 · Cited by 22
Simulated quantum annealing of double-well and multiwell potentials
Inack, E. M., & Pilati, S. (2015) · Physical Review E · doi:10.1103/physreve.92.053304 · Cited by 19
Quantum Annealing and Thermalization: Insights from Integrability
Li, F., Chernyak, V., & Sinitsyn, N. A. (2018) · Physical Review Letters · doi:10.1103/physrevlett.121.190601 · Cited by 20
Emergent kinetic constraints, ergodicity breaking, and cooperative dynamics in noisy quantum systems
Everest, B., Marcuzzi, M., Garrahan, J. P., & Lesanovsky, I. (2016) · Physical review. E · doi:10.1103/physreve.94.052108 · Cited by 20
Quantum computing reduces systemic risk in financial networks
Aboussalah, A. M., Chi, C., & Lee, C.-G. (2023) · Scientific Reports · doi:10.1038/s41598-023-30710-z · Cited by 20
Wen, J., Wang, Z., Huang, Z., Cai, D., Jia, B., Cao, C., Ma, Y., Wei, H., Wen, K., & Qian, L. (2023) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-023-2147-3 · Cited by 20
Error reduction in quantum annealing using boundary cancellation: Only the end matters
Venuti, L. C., & Lidar, D. A. (2018) · Physical Review A · doi:10.1103/physreva.98.022315 · Cited by 19
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
Using quantum annealers to calculate ground state properties of molecules
Copenhaver, J., Wasserman, A., & Wehefritz-Kaufmann, B. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0030397 · Cited by 19
An Ensemble Approach for Compressive Sensing with Quantum Annealers
Ayanzadeh, R., Halem, M., & Finin, T. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323509 · Cited by 18
Quantum annealing algorithms for Boolean tensor networks
Pelofske, E., Hahn, G., O’Malley, D., Djidjev, H. N., & Alexandrov, B. S. (2022) · Scientific Reports · doi:10.1038/s41598-022-12611-9 · Cited by 21
Quantum annealing – foundations and frontiers
Cohen, E., & Tamir, B. (2015) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2015-02345-1 · 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
Multiple Query Optimization Using a Gate-Based Quantum Computer
Fankhauser, T., Solèr, M. E., Füchslin, R. M., & Stockinger, K. (2023) · IEEE Access · doi:10.1109/access.2023.3324253 · Cited by 43
Quantum-inspired encoding enhances stochastic sampling of soft matter systems
Slongo, F., Hauke, P., Faccioli, P., & Micheletti, C. (2023) · Science Advances · doi:10.1126/sciadv.adi0204 · Cited by 18
Dynamic adaptive quantum approximate optimization algorithm for shallow, noise-resilient circuits
Yanakiev, N., Mertig, N., Long, C. K., & Arvidsson-Shukur, D. R. M. (2024) · Physical Review A · doi:10.1103/physreva.109.032420 · Cited by 26
Improved bounds for eigenpath traversal
Chiang, H.-T. L., Xu, G., & Somma, R. D. (2014) · Physical Review A · doi:10.1103/physreva.89.012314 · Cited by 18
Experimental and Theoretical Study of Thermodynamic Effects in a Quantum Annealer
Kadowaki, T., & Ohzeki, M. (2019) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.88.061008 · Cited by 16
Schuld, M., & Petruccione, F. (2016) · Springer eBooks · doi:10.1007/978-1-4899-7502-7_913-1 · Cited by 19
Anomalous behavior of the energy gap in the one-dimensional quantum XY model
Okuyama, M., Yamanaka, Y., Nishimori, H., & Rams, M. M. (2015) · Physical Review E · doi:10.1103/physreve.92.052116 · Cited by 14
Urushibata, M., Ohzeki, M., & Tanaka, K. (2022) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.91.074008 · Cited by 17
Programming physical quantum systems with pulse-level control
Smith, K. N., Ravi, G. S., Alexander, T., Bronn, N. T., Carvalho, A. R. R., Cervera-Lierta, A., Chong, F. T., Chow, J. M., Cubeddu, M., Hashim, A., et al. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.900099 · Cited by 24
Sampling rare conformational transitions with a quantum computer
Ghamari, D., Hauke, P., Covino, R., & Faccioli, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-20032-x · Cited by 18
Mukherjee, S., Rajak, A., & Chakrabarti, B. (2017) · Physical Review E · doi:10.1103/physreve.97.022146 · Cited by 16
Self-citationA Quantum Algorithm for Model-Independent Searches for New Physics
Matchev, K. T., Shyamsundar, P., & Smolinsky, J. (2023) · Letters in High Energy Physics · doi:10.31526/lhep.2023.301 · Cited by 19
Teacher-Student Learning for a Binary Perceptron with Quantum Fluctuations
Arai, S., Ohzeki, M., & Tanaka, K. (2021) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.90.074002 · Cited by 17
Improved bound on entropy production in a quantum annealer
Campisi, M., & Buffoni, L. (2021) · Physical review. E · doi:10.1103/physreve.104.l022102 · Cited by 16
Numerical gate synthesis for quantum heuristics on bosonic quantum processors
Özgüler, A. B., & Venturelli, D. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.900612 · Cited by 18
Dynamics of the quantum search and quench-induced first-order phase transitions
Coulamy, I., Saguia, A., & Sarandy, M. S. (2017) · Physical review. E · doi:10.1103/physreve.95.022127 · Cited by 16
A double-slit proposal for quantum annealing
Munoz-Bauza, H., Chen, H., & Lidar, D. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0160-0 · Cited by 16
Greedy parameter optimization for diabatic quantum annealing
Kadowaki, T., & Nishimori, H. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0416 · Cited by 15
FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes
Ayanzadeh, R., Alavisamani, N., Das, P., & Qureshi, M. K. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3575741 · Cited by 22
ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers
Chen, Z., Xiao, Z., Akl, M., Leugering, J., Olajide, O., Malik, A., Dennler, N., Harper, C., Bose, S., Gonzalez, H. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58231-5 · Cited by 20
Efficient Detection of Strong-to-Weak Spontaneous Symmetry Breaking via the Rényi-1 Correlator
Weinstein, Z. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.150405 · Cited by 28
Nishimura, K., Nishimori, H., Ochoa, A. J., & Katzgraber, H. G. (2016) · Physical review. E · doi:10.1103/physreve.94.032105 · Cited by 17
Parity Quantum Optimization: Encoding Constraints
Drieb-Schön, M., Ender, K., Javanmard, Y., & Lechner, W. (2023) · Quantum · doi:10.22331/q-2023-03-17-951 · Cited by 25
Bound state dynamics in the long-range spin- 12 XXZ model
Macrì, T., Lepori, L., Pagano, G., Lewenstein, M., & Barbiero, L. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.104.214309 · Cited by 15
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
Digital-analog quantum computation with arbitrary two-body Hamiltonians
de Andoin, M. G., Sáiz, Á., Pérez-Fernández, P., Lamata, L., Oregi, I., & Sanz, M. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013280 · Cited by 20
Graphene-based room-temperature implementation of a modified Deutsch–Jozsa quantum algorithm
Dragoman, D., & Dragoman, M. (2015) · Nanotechnology · doi:10.1088/0957-4484/26/48/485201 · Cited by 15
Superconducting qubit circuit emulation of a vector spin-1/2
Kerman, A. J. (2019) · New Journal of Physics · doi:10.1088/1367-2630/ab2ee7 · Cited by 15
Pattern-recalling processes in quantum Hopfield networks far from saturation
Inoue, J. (2011) · Institutional Repositories DataBase (IRDB) · Cited by 15
Using quantum computing to solve the maximal covering location problem
Giraldo-Quintero, A., Pulido, J. G. L., Duque, J. C., & Sierra-Sosa, D. (2022) · Computational Urban Science · doi:10.1007/s43762-022-00070-x · Cited by 13
Kondappan, M., Chaudhary, M., Ilo-Okeke, E. O., Ivannikov, V., & Byrnes, T. (2023) · Physical Review A · doi:10.1103/physreva.107.042616 · Cited by 18
Quantum Communication Networks for Energy Applications: Review and Perspective
Paudel, H. P., Crawford, S. E., Lee, Y., Shugayev, R., Leuenberger, M. N., Syamlal, M., Ohodnicki, P. R., Lu, P., Mollot, D., & Duan, Y. (2023) · Advanced Quantum Technologies · doi:10.1002/qute.202300096 · Cited by 19
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks
Lü, B., Gao, Y., Wen, K., & Wang, C. (2023) · Quantum · doi:10.22331/q-2023-10-24-1151 · Cited by 14
Narasimhan, P., Humeniuk, S., Roy, A., & Drouin-Touchette, V. (2024) · Physical review. B./Physical review. B · doi:10.1103/physrevb.110.054432 · Cited by 17
Ising formulation of associative memory models and quantum annealing recall
Santra, S., Shehab, O., & Balu, R. (2017) · Physical Review A · doi:10.1103/physreva.96.062330 · Cited by 16
Implementation of a quantum adiabatic algorithm for factorization on two qudits
Zobov, V. E., & Ermilov, A. S. (2012) · Journal of Experimental and Theoretical Physics · doi:10.1134/s106377611205007x · Cited by 14
Pattern-recalling processes in quantum Hopfield networks far from saturation
Inoue, J. (2011) · Journal of Physics Conference Series · doi:10.1088/1742-6596/297/1/012012 · Cited by 17
Entanglement in a quantum neural network based on quantum dots
Altaĭsky, M. V., Zol’nikova, N. N., Kaputkina, N. E., Крылов, В. А., Lozovik, Y. E., & Dattani, N. S. (2017) · Photonics and Nanostructures - Fundamentals and Applications · doi:10.1016/j.photonics.2017.02.001 · Cited by 13
Viewing vanilla quantum annealing through spin glasses
Katzgraber, H. G. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab6ba · Cited by 14
Takada, K., Yamashiro, Y., & Nishimori, H. (2020) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.89.044001 · Cited by 14
Simulating the operation of a quantum computer in a dissipative environment
Zhang, S., Chen, Y., & Shi, Q. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0185263 · Cited by 15
VanQver: the variational and adiabatically navigated quantum eigensolver
Matsuura, S., Yamazaki, T., Senicourt, V., Huntington, L., & Zaribafiyan, A. (2020) · New Journal of Physics · doi:10.1088/1367-2630/ab8080 · Cited by 13
Investigating the Chinese postman problem on a quantum annealer
Siloi, I., Carnevali, V., Pokharel, B., Fornari, M., & Felice, R. D. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00031-9 · Cited by 15
Quantum and classical annealing in a continuous space with multiple local minima
Koh, Y. W., & Nishimori, H. (2022) · Physical Review A · doi:10.1103/physreva.105.062435 · Cited by 13
Effectiveness of quantum annealing for continuous-variable optimization
Arai, S., Oshiyama, H., & Nishimori, H. (2023) · Physical Review A · doi:10.1103/physreva.108.042403 · Cited by 13
Sadhukhan, D., Prabhu, R., Sen, A., & Sen, U. (2016) · Physical review. E · doi:10.1103/physreve.93.032115 · Cited by 12
Kibble-Zurek mechanism in simulated annealing and quantum annealing
Suzuki, S. (2011) · Journal of Physics Conference Series · doi:10.1088/1742-6596/302/1/012046 · Cited by 12
Factorization in spin systems under general fields and separable ground-state engineering
Cerezo, M., Rossignoli, R., & Canosa, N. (2016) · Physical Review A · doi:10.1103/physreva.94.042335 · Cited by 13
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
