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
Hybrid quantum-classical algorithms in the noisy intermediate-scale quantum era and beyond
Callison, A., & Chancellor, N. (2022) · Physical Review A · doi:10.1103/physreva.106.010101 · Cited by 172
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
Botifoll, M., Pinto‐Huguet, I., & Arbiol, J. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00377e · Cited by 147
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 125
Self-citationQuantum beetle antennae search: a novel technique for the constrained portfolio optimization problem
Khan, A. T., Cao, X., Li, S., Hu, B., & Katsikis, V. N. (2021) · Science China Information Sciences · doi:10.1007/s11432-020-2894-9 · Cited by 98
Quantum Computational Finance: Quantum Algorithm for Portfolio Optimization
Rebentrost, P., & Lloyd, S. (2024) · Künstliche Intell. · doi:10.1007/s13218-024-00870-9 · Cited by 128
Benchmarking Quantum Annealing Controls with Portfolio Optimization
Grant, E., Humble, T. S., & Stump, B. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.15.014012 · Cited by 90
Domain wall encoding of discrete variables for quantum annealing and QAOA
Chancellor, N. (2019) · Quantum Science and Technology · doi:10.1088/2058-9565/ab33c2 · Cited by 90
Dynamics of reverse annealing for the fully connected p -spin model
Yamashiro, Y., Ohkuwa, M., Nishimori, H., & Lidar, D. A. (2019) · Physical Review A · doi:10.1103/physreva.100.052321 · Cited by 76
Domain wall encoding of integer variables for quantum annealing and QAOA [dataset]
Chancellor, N. (2019) · Durham Research Online (Durham University) · doi:10.15128/r27d278t029 · Cited by 75
Quantum Machine Learning: Recent Advances, Challenges, and Perspectives
Lamichhane, P., & Rawat, D. B. (2025) · IEEE Access · doi:10.1109/access.2025.3573244 · Cited by 86
Classical versus quantum models in machine learning: insights from a finance application
Alcazar, J., Leyton-Ortega, V., & Perdomo-Ortiz, A. (2020) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/ab9009 · Cited by 69
Evaluating the job shop scheduling problem on a D-wave quantum annealer
Carugno, C., Dacrema, M. F., & Cremonesi, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-10169-0 · Cited by 73
Toward pricing financial derivatives with an IBM quantum computer
Martin, A., Candelas, B., Rodríguez-Rozas, Á., Martín-Guerrero, J. D., Chen, X., Lamata, L., Orús, R., Solano, E., & Sanz, M. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.013167 · Cited by 74
Naik, A. S., Yeniaras, E., Hellstern, G., Prasad, G., & Vishwakarma, S. (2025) · Financial Innovation · doi:10.1186/s40854-025-00751-6 · Cited by 75
Implementation of Quantum Annealing: A Systematic Review
Yulianti, L. P., & Surendro, K. (2022) · IEEE Access · doi:10.1109/access.2022.3188117 · Cited by 68
Performance Comparison of Typical Binary-Integer Encodings in an Ising Machine
Tamura, K., Shirai, T., Katsura, H., Tanaka, S., & Togawa, N. (2021) · IEEE Access · doi:10.1109/access.2021.3081685 · Cited by 57
Portfolio optimization with digitized counterdiabatic quantum algorithms
Hegade, N. N., Chandarana, P., Paul, K., Chen, X., Albarrán-Arriagada, F., & Solano, E. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043204 · Cited by 62
Enhancing portfolio management using artificial intelligence: literature review
Šutienė, K., Schwendner, P., Șipoș, C., Lorenzo, L., Mirchev, M., Lameski, P., Kabašinskas, A., Tidjani, C., Öztürkkal, B., & Černevičienė, J. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1371502 · Cited by 94
Portfolio Optimisation Using the D-Wave Quantum Annealer
Phillipson, F., & Bhatia, H. S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77980-1_4 · Cited by 54
Quantum Algorithms for Mixed Binary Optimization Applied to Transaction Settlement
Braine, L., Egger, D. J., Glick, J., & Woerner, S. (2021) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2021.3063635 · Cited by 51
Multi-objective Quantum Annealing approach for solving flexible job shop scheduling in manufacturing
Schworm, P., Wu, X., Klar, M., Glatt, M. F., & Aurich, J. C. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.11.015 · Cited by 51
Deep neural networks for quantum circuit mapping
Acampora, G., & Schiattarella, R. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06009-3 · Cited by 56
Quantum Semantic Learning by Reverse Annealing of an Adiabatic Quantum Computer
Rocutto, L., Destri, C., & Prati, E. (2020) · Advanced Quantum Technologies · doi:10.1002/qute.202000133 · Cited by 47
Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware
Schönberger, M., Scherzinger, S., & Mauerer, W. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3588946 · Cited by 49
Short-depth QAOA circuits and quantum annealing on higher-order ising models
Pelofske, E., Bärtschi, A., & Eidenbenz, S. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00825-w · Cited by 49
Quantum Annealing with Longitudinal Bias Fields
Graß, T. (2019) · Physical Review Letters · doi:10.1103/physrevlett.123.120501 · Cited by 46
Prospects and challenges of quantum finance
Bouland, A., van Dam, W., Joorati, H., Kerenidis, I., & Prakash, A. (2020) · arXiv · doi:10.48550/arxiv.2011.06492 · Cited by 46
Adame, J. I., & McMahon, P. L. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab935a · Cited by 46
Travel time optimization on multi-AGV routing by reverse annealing
Haba, R., Ohzeki, M., & Tanaka, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-22704-0 · Cited by 43
Why and When Pausing is Beneficial in Quantum Annealing
Chen, H., & Lidar, D. A. (2020) · Physical Review Applied · doi:10.1103/physrevapplied.14.014100 · Cited by 41
Multi-car paint shop optimization with quantum annealing
Yarkoni, S., Alekseyenko, A., Streif, M., Dollen, D. V., Neukart, F., & Bäck, T. (2021) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce52317.2021.00019 · Cited by 43
Performance of Domain-Wall Encoding for Quantum Annealing [dataset]
Chen, J., & Chancellor, N. (2021) · Durham Research Online (Durham University) · doi:10.15128/r11j92g748d · Cited by 43
Energetic Perspective on Rapid Quenches in Quantum Annealing
Callison, A., Festenstein, M. Z., Chen, J., Nita, L., Kendon, V. M., & Chancellor, N. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.010338 · Cited by 41
Mean field analysis of reverse annealing for code-division multiple-access multiuser detection
Arai, S., Ohzeki, M., & Tanaka, K. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033006 · Cited by 36
Using quantum amplitude amplification in genetic algorithms
Acampora, G., Schiattarella, R., & Vitiello, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118203 · Cited by 36
Quantum Annealing-Based Software Components
Krüger, T., & Mauerer, W. (2020) · doi:10.1145/3387940.3391472 · Cited by 35
Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
Lang, J. B., Zielinski, S., & Feld, S. (2022) · Applied Sciences · doi:10.3390/app122312288 · Cited by 31
How to Solve Combinatorial Optimization Problems Using Real Quantum Machines: A Recent Survey
Heng, S., Kim, D., Kim, T.-K., & Han, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3218908 · Cited by 34
Solving the resource constrained project scheduling problem with quantum annealing
Armas, L. F. P., Creemers, S., & Deleplanque, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-67168-6 · Cited by 39
QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction
Dutta, S., Innan, N., Marchisio, A., Yahia, S. B., & Shafique, M. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.10303 · Cited by 38
An Application of Quantum Annealing Computing to Seismic Inversion
Souza, A. M., Martins, E. O., Roditi, I., Sá, N., Sarthour, R. S., & Oliveira, I. S. (2022) · Frontiers in Physics · doi:10.3389/fphy.2021.748285 · Cited by 35
Solving larger maximum clique problems using parallel quantum annealing
Pelofske, E., Hahn, G., & Djidjev, H. N. (2023) · Quantum Information Processing · doi:10.1007/s11128-023-03962-x · Cited by 31
Improving the Solving of Optimization Problems: A Comprehensive Review of Quantum Approaches
Volpe, D., Orlandi, G., & Turvani, G. (2025) · Quantum Reports · doi:10.3390/quantum7010003 · Cited by 32
Experimental implementation of quantum-walk-based portfolio optimization
Qu, D., Matwiejew, E., Wang, K., Wang, J. B., & Xue, P. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad27e9 · Cited by 28
Multi-Objective Portfolio Optimization Using a Quantum Annealer
Aguilera, E., de Jong, J., Phillipson, F., Taamallah, S., & Vos, M. (2024) · Mathematics · doi:10.3390/math12091291 · Cited by 26
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
Enhancing Quantum Machine Learning Algorithms for Optimized Financial Portfolio Management
Bhasin, N. K., Kadyan, S., Santosh, K., Ramya, H. P., Changala, R., & Bala, B. K. (2024) · IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) · doi:10.1109/incos59338.2024.10527612 · Cited by 25
A Leap From Theory to Reality: Knowledge Visualization of Quantum Computing
Rawat, K. S., & Sharma, T. K. (2024) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2024.3466949 · Cited by 26
Efficient Hamiltonian simulation for solving option price dynamics
Gonzalez-Conde, J., Rodríguez‐Rozas, Á., Solano, E., & Sanz, M. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.043220 · Cited by 36
Evolving objective function for improved variational quantum optimization
Kolotouros, I., & Wallden, P. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.023225 · Cited by 24
Tatsumura, K., Hidaka, R., Nakayama, J., Kashimata, T., & Yamasaki, M. (2023) · IEEE Access · doi:10.1109/access.2023.3326816 · Cited by 24
Variationally scheduled quantum simulation
Matsuura, S., Buck, S., Senicourt, V., & Zaribafiyan, A. (2021) · Physical Review A · doi:10.1103/physreva.103.052435 · Cited by 27
Benchmarking Quantum(-Inspired) Annealing Hardware on Practical Use Cases
Huang, T., Xu, J., Luo, T., Gu, X., Goh, R. S. M., & Wong, W. (2022) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3219257 · Cited by 25
Anomaly detection speed-up by quantum restricted Boltzmann machines
Moro, L., & Prati, E. (2023) · Communications Physics · doi:10.1038/s42005-023-01390-y · Cited by 27
Routing and scheduling optimization for urban air mobility fleet management using quantum annealing
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
Mapping graph coloring to quantum annealing
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
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
Advanced anneal paths for improved quantum annealing
Pelofske, E., Hahn, G., & Djidjev, H. N. (2020) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce49297.2020.00040 · Cited by 19
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
“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 22
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 26
Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization
Beloborodov, D., Ulanov, A., Foerster, J. N., Whiteson, S., & Lvovsky, A. (2020) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/abc328 · Cited by 22
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
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
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 19
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
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
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
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 17
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
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
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
Sawaya, N. P. D., Schmitz, A. T., & Hadfield, S. (2023) · Quantum · doi:10.22331/q-2023-09-14-1111 · Cited by 28
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 20
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
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
Counterdiabatic reverse annealing
Passarelli, G., & Lucignano, P. (2023) · Physical Review A · doi:10.1103/physreva.107.022607 · Cited by 15
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 26
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
Yu, S., & Nabil, T. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.730685 · Cited by 13
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 27
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
Fluctuation-guided search in quantum annealing
Chancellor, N. (2020) · Physical Review A · doi:10.1103/physreva.102.062606 · Cited by 11
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 15
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
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
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
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
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 12
Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios
Catalano, F., Nasello, L., & Guterding, D. (2024) · Risks · doi:10.3390/risks12040066 · Cited by 11
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 16
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
A double-slit proposal for quantum annealing
Munoz-Bauza, H., Chen, H., & Lidar, D. A. (2019) · eScholarship (California Digital Library) · Cited by 10
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
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
