Reverse quantum annealing approach to portfolio optimization problems
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Forward and reverse annealing have been applied to the nurse scheduling problem and portfolio optimization.
“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]
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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 · see citing works
Sharpe, W. F. (1966) · The Journal of Business · doi:10.1086/294846 · Cited by 3,937 · see citing works
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 · see citing works
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 · see citing works
Self-citationSortino, F. A., & van der Meer, R. (1991) · The Journal of Portfolio Management · doi:10.3905/jpm.1991.409343 · Cited by 605 · see citing works
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 · see citing works
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× · see citing works
“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 356 · see citing works
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 · see citing works
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× · see citing works
“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].”
BackgroundComputational multiqubit tunnelling in programmable quantum annealers
Boixo, S., Smelyanskiy, V., Shabani, A., Isakov, S. V., Dykman, M. I., Denchev, V. S., Amin, M. H. S., Smirnov, A. Y., Mohseni, M., & Neven, H. (2016) · Nature Communications · doi:10.1038/ncomms10327 · Cited by 232 · see citing works
Quantum Optimization of Fully Connected Spin Glasses
Venturelli, D., Mandrà, S., Knysh, S., O’Gorman, B., Biswas, R., & Smelyanskiy, V. (2015) · Physical Review X · doi:10.1103/physrevx.5.031040 · Cited by 209 · see citing works
Self-citationSolving 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 202 · see citing works
Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer
Rosenberg, G., Haghnegahdar, P., Goddard, P., Carr, P., Wu, K. J., & de Prado, M. L. (2015) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2574703 · Cited by 191 · see citing works
Using genetic algorithm to support portfolio optimization for index fund management
Oh, K. J., Kim, T. Y., & Min, S. (2004) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.10.014 · Cited by 183 · see citing works
Selecting Portfolios with Fixed Costs and Minimum Transaction Lots
Kellerer, H. G., Mansini, R., & Speranza, M. G. (2000) · Annals of Operations Research · doi:10.1023/a:1019279918596 · Cited by 163 · see citing works
Fast clique minor generation in Chimera qubit connectivity graphs
Boothby, T., King, A. D., & Roy, A. (2015) · Quantum Information Processing · doi:10.1007/s11128-015-1150-6 · Cited by 161 · see citing works
Quantum Speed-Ups for Solving Semidefinite Programs
Brandao, F. G. S. L., & Svore, K. M. (2017) · 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS) · doi:10.1109/focs.2017.45 · Cited by 142 · see citing works
Quantum Annealing Implementation of Job-Shop SchedulingPreprint
Venturelli, D., Marchand, D. J. J., & Rojo, G. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.08479 · Cited by 129 · see citing works
Self-citationQuantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management
Stollenwerk, T., O'Gorman, B., Venturelli, D., Mandra, S., Rodionova, O., Ng, H., Sridhar, B., Rieffel, E. G., & Biswas, R. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2891235 · Cited by 129 · see citing works
Self-citationQuantum Computational Finance: Quantum Algorithm for Portfolio Optimization
Rebentrost, P., & Lloyd, S. (2024) · Künstliche Intell. · doi:10.1007/s13218-024-00870-9 · Cited by 128 · see citing works
Modernizing quantum annealing using local searches
Chancellor, N. (2016) · New Journal of Physics · doi:10.1088/1367-2630/aa59c4 · Cited by 120 · see citing works
Power of Pausing: Advancing Understanding of Thermalization in Experimental Quantum Annealers
Marshall, J., Venturelli, D., Hen, I., & Rieffel, E. G. (2019) · Physical Review Applied · doi:10.1103/physrevapplied.11.044083 · Cited by 119 · see citing works
Self-citationReverse annealing for the fully connected p -spin model
Ohkuwa, M., Nishimori, H., & Lidar, D. A. (2018) · Physical Review A · doi:10.1103/physreva.98.022314 · Cited by 109 · see citing works
A deceptive step towards quantum speedup detection
Mandrà, S., & Katzgraber, H. G. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aac8b2 · Cited by 82 · see citing works
Benchmarking a quantum annealing processor with the time-to-target metric
King, J., Yarkoni, S., Nevisi, M. M., Hilton, J. P., & McGeoch, C. C. (2015) · arXiv · doi:10.48550/arxiv.1508.05087 · Cited by 77 · see citing works
A Hybrid Quantum-Classical Approach to Solving Scheduling Problems
Tran, T. T., Do, M., Rieffel, E. G., Frank, J., Wang, Z., O’Gorman, B., Venturelli, D., & Beck, J. C. (2021) · Proceedings of the International Symposium on Combinatorial Search · doi:10.1609/socs.v7i1.18390 · Cited by 73 · see citing works
Self-citationNested quantum annealing correction
Vinci, W., Albash, T., & Lidar, D. A. (2016) · npj Quantum Information · doi:10.1038/npjqi.2016.17 · Cited by 57 · see citing works
Adame, J. I., & McMahon, P. L. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab935a · Cited by 46 · see citing works
Portfolio Optimization: Applications in Quantum Computing
Marzec, M. (2016) · Wiley eBooks · doi:10.1002/9781118593486.ch4 · Cited by 42 · see citing works
Algorithm engineering for a quantum annealing platform
King, A. D., & McGeoch, C. C. (2014) · arXiv · doi:10.48550/arxiv.1410.2628 · Cited by 39 · see citing works
A Performance Estimator for Quantum Annealers: Gauge selection and Parameter SettingPreprint
Perdomo‐Ortiz, A., Fluegemann, J., Biswas, R., & Smelyanskiy, V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.01083 · Cited by 37 · see citing works
Boosting quantum annealer performance via sample persistence
Karimi, H., & Rosenberg, G. (2017) · Quantum Information Processing · doi:10.1007/s11128-017-1615-x · Cited by 34 · see citing works
Job, J., & Lidar, D. A. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aabd9b · Cited by 33 · see citing works
Conditionally fitted Sharpe performance with an application to hedge fund rating
Darolles, S., & Gouriéroux, C. (2009) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2009.08.025 · Cited by 28 · see citing works
Lin, D., Li, X., & Li, M. (2005) · Lecture notes in computer science · doi:10.1007/11539902_99 · Cited by 25 · see citing works
Efficient population transfer via non-ergodic extended states in quantum spin glassPreprint
Kechedzhi, K., Smelyanskiy, V., McClean, J. R., Denchev, V. S., Mohseni, M., Isakov, S. V., Boixo, S., Altshuler, B. L., & Neven, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.04792 · Cited by 23 · see citing works
Low Rank Non-Negative Matrix Factorization with D-Wave 2000QPreprint
Ottaviani, D., & Amendola, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.08721 · Cited by 23 · see citing works
Genetic algorithm-based portfolio optimization with higher moments in global stock markets
Kshatriya, S., & Prasanna, K. (2018) · The Journal of Risk · doi:10.21314/jor.2018.380 · Cited by 16 · see citing works
Hamerly, R., Inagaki, T., McMahon, P. L., Venturelli, D., Marandi, A., Onodera, T., Ng, E., Langrock, C., Inaba, K., Honjo, T., et al. (2018) · arXiv (Cornell University) · Cited by 14 · see citing works
Self-citationPractical Portfolio Optimization
Siwale, I. (2013) · doi:10.13140/rg.2.1.2491.2883/1 · Cited by 4 · see citing works
Flagged first, then by citations
3 of the 44 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [9][9] Harry Markowitz. Portfolio selection. The journal of finance , 7(1):77-91, 1952. -
- [12][12] Alexei Kondratyev and George Giorgidze. Evolutionary algos for mva optimisation. Risk , 30(12):136-141, 2017. -
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Haba, R., Mano, T., Ueda, R., Ebe, G., Takeda, K., Terabe, M., & Ohzeki, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-86843-w · Cited by 24 · see citing works
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SILVA, C. V. D., Aguiar, A., Lima, P. M. V., & Dutra, I. (2020) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00028-4 · Cited by 23 · see citing works
Quantum Computing for Artificial Intelligence Based Mobile Network Optimization
Ahmed, F., & Mähönen, P. (2021) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc50174.2021.9569339 · Cited by 21 · see citing works
Advanced anneal paths for improved quantum annealing
Pelofske, E., Hahn, G., & Djidjev, H. (2020) · International Conference on Quantum Computing and Engineering · doi:10.1109/qce49297.2020.00040 · Cited by 23 · see citing works
Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study
Gemeinhardt, F., Garmendia, A., Wimmer, M., Weder, B., & Leymann, F. (2024) · ACM Computing Surveys · doi:10.1145/3620668 · Cited by 22 · see citing works
“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].”
MethodBenchmarking embedded chain breaking in quantum annealing *
Grant, E., & Humble, T. S. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ac26d2 · Cited by 20 · see citing works
Single-Qubit Fidelity Assessment of Quantum Annealing Hardware
Nelson, J., Vuffray, M., Lokhov, A. Y., & Coffrin, C. (2021) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2021.3092710 · Cited by 21 · see citing works
Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization
Beloborodov, D., Ulanov, A. E., Foerster, J., Whiteson, S., & Lvovsky, A. I. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abc328 · Cited by 17 · see citing works
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 17 · see citing works
Experimental test of search range in quantum annealing
Chancellor, N., & Kendon, V. M. (2021) · Physical Review A · doi:10.1103/physreva.104.012604 · Cited by 15 · see citing works
Efficient Algorithm for Binary Quadratic Problem by Column Generation and Quantum Annealing
Hirama, S., & Ohzeki, M. (2023) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.92.113002 · Cited by 16 · see citing works
Efficiency optimization in quantum computing: balancing thermodynamics and computational performance
Śmierzchalski, T., Mzaouali, Z., Deffner, S., & Gardas, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-55314-z · Cited by 15 · see citing works
Practical designs for permutation-symmetric problem Hamiltonians on hypercubes
Dodds, A. B., Kendon, V. M., Adams, C. S., & Chancellor, N. (2019) · Physical Review A · doi:10.1103/physreva.100.032320 · Cited by 14 · see citing works
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 · see citing works
Virtual Screening of Chemical Space Based on Quantum Annealing
Tanaka, T., Sako, M., Chiba, M., Lee, C., Cha, H., & Ohzeki, M. (2023) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.92.023001 · Cited by 16 · see citing works
Backtesting Quantum Computing Algorithms for Portfolio Optimization
Carrascal, G., Hernamperez, P., Botella, G., & Barrio, A. A. D. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3337328 · Cited by 15 · see citing works
Towards An End-To-End Approach For Quantum Principal Component Analysis
Dri, E., Aita, A., Fioravanti, T., Franco, G., Giusto, E., Ranieri, G., Corbelletto, D., & Montrucchio, B. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.10175 · Cited by 15 · see citing works
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 15 · see citing works
Sawaya, N. P. D., Schmitz, A. T., & Hadfield, S. (2023) · Quantum · doi:10.22331/q-2023-09-14-1111 · Cited by 16 · see citing works
Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem
Gunjan, A., & Bhattacharyya, S. (2024) · Evolutionary Intelligence · doi:10.1007/s12065-024-00929-4 · Cited by 14 · see citing works
Constructing driver Hamiltonians for optimization problems with linear constraints
Leipold, H., & Spedalieri, F. M. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ac16b8 · Cited by 14 · see citing works
Standard quantum annealing outperforms adiabatic reverse annealing with decoherence
Passarelli, G., Yip, K.-W., Lidar, D. A., & Lucignano, P. (2022) · Physical Review A · doi:10.1103/physreva.105.032431 · Cited by 14 · see citing works
Counterdiabatic reverse annealing
Passarelli, G., & Lucignano, P. (2023) · Physical Review A · doi:10.1103/physreva.107.022607 · Cited by 15 · see citing works
Quadratic Unconstrained Binary Formulation for Traffic Signal Optimization on Real-World Maps
Shikanai, R., Ohzeki, M., & Tanaka, K. (2025) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.94.024001 · Cited by 24 · see citing works
Simulation of implementable quantum-assisted genetic algorithm
Supasil, J., Pathumsoot, P., & Suwanna, S. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1719/1/012102 · Cited by 13 · see citing works
Yu, S., & Nabil, T. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.730685 · Cited by 13 · see citing works
Quantum-machine-assisted drug discovery
Zhou, Y., Chen, J., Cheng, J., Cao, X., Zhang, Y., Karemore, G., Žitnik, M., Chong, F. T., Liu, J., Fu, T., et al. (2026) · npj Drug Discovery. · doi:10.1038/s44386-025-00033-2 · Cited by 19 · see citing works
A Quantum-Inspired Bilevel Optimization Algorithm for the First Responder Network Design Problem
Karahalios, A., TAYUR, S. R., Tenneti, A., Pashapour, A., Salman, F. S., & Yıldız, B. (2024) · INFORMS journal on computing · doi:10.1287/ijoc.2024.0574 · Cited by 12 · see citing works
Fluctuation-guided search in quantum annealing
Chancellor, N. (2020) · Physical Review A · doi:10.1103/physreva.102.062606 · Cited by 11 · see citing works
Deep learning optimal quantum annealing schedules for random Ising models
Hegde, P. R., Passarelli, G., Cantele, G., & Lucignano, P. (2023) · New Journal of Physics · doi:10.1088/1367-2630/ace547 · Cited by 13 · see citing works
Initial State Encoding via Reverse Quantum Annealing and H-Gain Features
Pelofske, E., Hahn, G., & Djidjev, H. N. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3319586 · Cited by 15 · see citing works
Graver Bases via Quantum Annealing with Application to Non-Linear Integer ProgramsPreprint
Alghassi, H., Dridi, R., & Tayur, S. (2019) · arXiv.org · doi:10.48550/arxiv.1902.04215 · Cited by 19 · see citing works
Perelshtein, M. R., Pakhomchik, A. I., Мельников, А. А., Podobrii, M., Termanova, A., Kreidich, I., Nuriev, B., Iudin, S., Mansell, C., & Vinokur, V. M. (2023) · Quantum · doi:10.22331/q-2023-11-21-1186 · Cited by 11 · see citing works
Noise amplification at spin-glass bottlenecks of quantum annealing: A solvable model
Roberts, D., Cincio, Ł., Saxena, A., Petukhov, A. G., & Knysh, S. (2020) · Physical Review A · doi:10.1103/physreva.101.042317 · Cited by 11 · see citing works
Solving the Shipment Rerouting Problem with Quantum Optimization Techniques
Yarkoni, S., Huck, A., Schülldorf, H., Speitkamp, B., Tabrizi, M. S., Leib, M., Bäck, T., & Neukart, F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87672-2_33 · Cited by 11 · see citing works
Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios
Catalano, F., Nasello, L., & Guterding, D. (2024) · Risks · doi:10.3390/risks12040066 · Cited by 11 · see citing works
Exploration of new chemical materials using black-box optimization with the D-wave quantum annealer
Doi, M., Nakao, Y., Tanaka, T., Sako, M., & Ohzeki, M. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1286226 · Cited by 12 · see citing works
Review of quantum algorithms for medicine, finance and logistics
Ciacco, A., Guerriero, F., & Macrina, G. (2025) · Soft Computing · doi:10.1007/s00500-025-10540-z · Cited by 12 · see citing works
A double-slit proposal for quantum annealing
Munoz-Bauza, H., Chen, H., & Lidar, D. A. (2019) · eScholarship (California Digital Library) · Cited by 10 · see citing works
A Method for Reducing Time-to-Solution in Quantum Annealing Through Pausing
Zielewski, M. R., & Takizawa, H. (2022) · International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia) · doi:10.1145/3492805.3492815 · Cited by 10 · see citing works
- 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 · 2018: 1
- 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
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