The DOI matches its Crossref record; 41 of the 44 references it cites were found in a registry or an index.
Reverse quantum annealing approach to portfolio optimization problems
Open accessOverview
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?
The cited source says what the claim attributes to it.
In the citing paper
“When it comes to more advanced quantum annealing methods, forward as well as reverse annealing have been applied to the nurse scheduling problem [69] and portfolio optimization [140].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [140]
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
“Options, Futures, and Other Derivatives”
(2016) · AMBER – ABBS Management Business and Entrepreneurship Review · doi:10.23874/amber/2016/v7/i1/121351 · Cited by 5,080
Sharpe, W. F. (1966) · The Journal of Business · doi:10.1086/294846 · Cited by 3,937
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
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
Hadfield, S., Wang, Z., O’Gorman, B., Rieffel, E. G., Venturelli, D., & Biswas, R. (2019) · Algorithms · doi:10.3390/a12020034 · Cited by 772
Self-citationSortino, F. A., & van der Meer, R. (1991) · The Journal of Portfolio Management · doi:10.3905/jpm.1991.409343 · Cited by 605
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
What is the Computational Value of Finite-Range Tunneling?
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 · cited 2×
“For this reason they are subject to extensive empirical investigation by several groups around the world, not only for scientific and research purposes [1, 2], but also for performance evaluation on structured real world optimization challenges [3, 4, 5].”
BackgroundOptimization Methods in Finance
Cornuéjols, G., & Tütüncü, R. H. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511753886 · Cited by 354
Hamerly, R., Inagaki, T., McMahon, P. L., Venturelli, D., Marandi, A., Onodera, T., Ng, E., Langrock, C., Inaba, K., Honjo, T., et al. (2019) · CaltechAUTHORS (California Institute of Technology) · Cited by 257
Self-citationDemonstration of a Scaling Advantage for a Quantum Annealer over Simulated Annealing
Albash, T., & Lidar, D. A. (2018) · Physical Review X · doi:10.1103/physrevx.8.031016 · Cited by 238 · cited 1×
“In particular, the annealing time is expected to be suboptimal for N ≤ 50, leading to a measurement of a flatter slope for the median TTS [33].”
Background
Research works printed but not linked: 2
- Kondratyev A, Giorgidze G (2017) Evolutionary algos for mva optimisation. Risk 30(12):136–141
- Markowitz H (1952) Portfolio selection. J Financ 7(1):77–91
Cited by
Sorted by citations
Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 2,231
Quantum annealing for industry applications: introduction and review
Yarkoni, S., Raponi, E., Bäck, T., & Schmitt, S. (2022) · Reports on Progress in Physics · doi:10.1088/1361-6633/ac8c54 · Cited by 341
Quantum bridge analytics I: a tutorial on formulating and using QUBO models
Glover, F., Kochenberger, G. A., Hennig, R., & Du, Y. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04634-2 · Cited by 369
Exploratory Combinatorial Optimization with Reinforcement Learning
Barrett, T. D., Clements, W. R., Foerster, J., & Lvovsky, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5723 · Cited by 185
Grover Adaptive Search for Constrained Polynomial Binary Optimization
Gilliam, A., Woerner, S., & Gonciulea, C. (2021) · Quantum · doi:10.22331/q-2021-04-08-428 · Cited by 153
Metadata
- Authors
- Venturelli, D., & Kondratyev, A.
- Affiliations
- Research Institute for Advanced Computer Science (US)
- Corresponding author
- Venturelli, Davide
- ORCID
- Venturelli, Davide
- Venue
- Quantum Machine Intelligence
- Published
- 2019-04-26
- Volume
- 1
- Issue
- 1-2
- Pages
- 17-30
- ISSN
- 2524-4906 (ISSN-L)
- 2524-4914
- OpenAlex
- W2897628955
- Cited by
- 233
- References
- 44
- Citations by year
- 2026: 21 · 2025: 44 · 2024: 38 · 2023: 32 · 2022: 29 · 2021: 34 · 2020: 23 · 2019: 11
- FWCI
- 11.42
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Quantum Machine Intelligence (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (164883)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Social Sciences › Decision Sciences › Management Science and Operations Research › Risk and Portfolio Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- portfolio optimization, mean-variance portfolio, D-Wave quantum annealer, hybrid quantum-classical algorithms, greedy local search, solution time
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
