The DOI matches its Crossref record; 60 of the 64 references it cites were found in a registry or an index.
A study of heuristic guesses for adiabatic quantum computation
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“Promising improvement strategies comprise non-stoquastic Hamiltonians [170], spatially inhomogeneous driving of the transverse field, and reverse annealing [205].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [205]
Citations
Cites
(1979) · Cited by 43,841
Messiah, A. (1961) · Cited by 16,012
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 · cited 1×
“He showed that using information about the structure of the problem as a heuristic guide can be used to enhance the performance of quantum search compared to the scheme proposed by Grover [ 18 ].”
MethodQuantum information and computation
Bennett, C. H., & DiVincenzo, D. P. (2000) · Nature · doi:10.1038/35005001 · Cited by 3,263
Introduction to the Theory of Computation
Sipser, M. (1996) · ACM SIGACT News · doi:10.1145/230514.571645 · Cited by 2,886
A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
Farhi, E., Goldstone, J., Gutmann, S., Lapan, J., Lundgren, A., & Preda, D. (2001) · Science · doi:10.1126/science.1057726 · Cited by 2,056
Simulated Quantum Computation of Molecular Energies
Aspuru-Guzik, A., Dutoi, A. D., Love, P. J., & Head-Gordon, M. (2005) · Science · doi:10.1126/science.1113479 · Cited by 1,418
Self-citationAnalytic and Algorithmic Solution of Random Satisfiability Problems
Mézard, M., Giorgio Parisi, A., & Zecchina, R. (2002) · Science · doi:10.1126/science.1073287 · Cited by 1,045
The Complexity of the Local Hamiltonian Problem
Kempe, J., Kitaev, A., & Regev, O. (2006) · SIAM Journal on Computing · doi:10.1137/s0097539704445226 · Cited by 648
Quantum search by local adiabatic evolution
Roland, J., & Cerf, N. J. (2002) · Physical Review A · doi:10.1103/physreva.65.042308 · Cited by 629
Research works printed but not linked: 1
- Farhi, E., Goldstone, J., Gutmann, S.: Quantum adiabatic evolution algorithms with different paths, quant-ph/0208135 (2002) · arXiv
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Search range in experimental quantum annealing [dataset]
Chancellor, N. (2020) · Durham Research Online (Durham University) · doi:10.15128/r2kp78gg42f · Cited by 1
Open quantum system dynamics for describing state transfer
Zuniga-Hansen, N. (2014) · OpenSIUC (Southern Illinois University Carbondale)
Path Independence in Adiabatic Quantum Computing for Hadamard Gate
Kosasih, J. S., Suhadi, S., & Zen, F. P. (2014) · Journal of Mathematical and Fundamental Sciences · doi:10.5614/j.math.fund.sci.2014.46.1.3
Numerical Studies of the Quantum Adiabatic Algorithm
Young, A. P., & Hen, I. (2015) · Journal of Physics Conference Series · doi:10.1088/1742-6596/640/1/012038
Benchmarks and Controls for Optimization with Quantum Annealing
Grant, E. (2020)
Mean-Field Analysis of Sourlas Codes with Adiabatic Reverse Annealing
Arai, S. (2021) · Springer eBooks · doi:10.1007/978-981-16-4095-7_13
Wang, H. (2021) · University of Minnesota Digital Conservancy (University of Minnesota)
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)
Kuji, H., Shingu, Y., Nikuni, T., Imoto, T., Sugisaki, K., & Matsuzaki, Y. (2025) · Physical Review A · doi:10.1103/physreva.111.042618
Osaba, E., & Villar-Rodríguez, E. (2025) · Frontiers in Physics · doi:10.3389/fphy.2025.1468348
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
Q-CHOP: Quantum constrained Hamiltonian optimization
Perlin, M. A., Shaydulin, R., Hall, B., Minssen, P., Li, C., Dubey, K., Rines, R., Anschuetz, E. R., Pistoia, M., & Gokhale, P. (2025) · ACM Transactions on Quantum Computing · doi:10.1145/3778864 · Cited by 1
Quantum annealing: optimisation, sampling, and many-body dynamics
Abel, S. A., Constantin, A., & Nutricati, L. A. (2026) · Contemporary Physics · doi:10.1080/00107514.2026.2675146 · Cited by 1
Extending the computational reach of quantum annealing using reverse annealing
Menger, L. J., Lippert, T., & Jattana, M. S. (2026) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-026-00562-3
Comparing quantum to simulated reverse annealing in mean-field models
Baldwin, C. L. (2026) · Physical Review A · doi:10.1103/ddv6-66hd
Metadata
- Authors
- Perdomo-Ortiz, A., Venegas-Andraca, S. E., & Aspuru-Guzik, A.
- Affiliations
- Tecnológico de Monterrey (MX)
- Venue
- Quantum Information Processing
- Published
- 2010-03-14
- Volume
- 10
- Issue
- 1
- Pages
- 33-52
- ISSN
- 1570-0755 (ISSN-L)
- 1573-1332
- OpenAlex
- W2078536001
- Cited by
- 111
- References
- 64
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 8 · 2023: 11 · 2022: 10 · 2021: 16 · 2020: 10 · 2019: 15 · 2018: 4 · 2017: 3 · 2016: 2 · 2015: 6 · 2014: 4 · 2013: 1
- FWCI
- 1.69
- Type
- journal article
- Language
- en
- Open access
- green
- nrs.harvard.edu
- Licence
- Locations
- Quantum Information Processing (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Greater South Information System
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- adiabatic quantum computation, 3-SAT, satisfiability, Hamming distance
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