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Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems
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“Finally, Ajagekar et al. [2] presented four examples of large-scale optimization problems solved with problem class-specific hybrid decomposition techniques: molecular conformation, job-shop-scheduling, manufacturing cell formation, and vehicle routing [2].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [2]
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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 · cited 1×
“Heuristic techniques such as simulated annealing [6], genetic algorithms [7], tabu search [8] and others have also grown popular owing to their easy implementation and little prior knowledge requirement of the optimization problem.”
MethodQuantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830 · cited 1×
“QC’s major applications can be perceived in areas of optimization, machine learning, cryptography, and quantum chemistry [16].”
BackgroundAlgorithms for quantum computation: discrete logarithms and factoring
Shor, P. W. (2002) · Proceedings 35th Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365700 · Cited by 8,713 · cited 1×
“This is evident from QC techniques like Shor’s algorithm for integer factorization [11], Grover’s search algorithm for unstructured databases [12], quantum algorithm for linear system of equations [13], and many more [14].”
MethodQuantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
Glover, F., & Laguna, M. (1997) · Cited by 5,679
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×
“This is evident from QC techniques like Shor’s algorithm for integer factorization [11], Grover’s search algorithm for unstructured databases [12], quantum algorithm for linear system of equations [13], and many more [14].”
Method(2002) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898718515 · Cited by 4,049
Partitioning procedures for solving mixed-variables programming problems
Benders, J. F. (1962) · Numerische Mathematik · doi:10.1007/bf01386316 · Cited by 3,958 · cited 2×
“The proposed hybrid QC-MIQP stepwise decomposition method is based on Bender’s decomposition [65], by considering the above manufacturing cell formation problem given in Eq.”
MethodQuantum Algorithm for Linear Systems of Equations
Harrow, A. W., Hassidim, A., & Lloyd, S. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.150502 · Cited by 3,325 · cited 1×
“This is evident from QC techniques like Shor’s algorithm for integer factorization [11], Grover’s search algorithm for unstructured databases [12], quantum algorithm for linear system of equations [13], and many more [14].”
MethodIsing formulations of many NP problems
Lucas, A. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00005 · Cited by 2,512 · cited 1×
“A Hamiltonian is a mathematical description of the physical system in terms of its energies and corresponds to the objective function of an optimization problem [29].”
Background
Research works printed but not linked: 1
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Items 101–191 of 191
Hybrid Algorithm of Linear Programming Relaxation and Quantum Annealing
Takabayashi, T., & Ohzeki, M. (2024) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.93.034001 · Cited by 11
Applying Quantum Computing to Solve Multicommodity Network Flow Problem
Niu, C., Rastogi, P., Soman, J. T., Tamuli, K., & Dixit, V. V. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3532640 · Cited by 10
“Ajagekar et al. [24] utilized the quantum computing to tackle various problems, including the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem.”
Li, S., Chen, L., Gui, X., He, D., Hu, J., Huang, Z., Lin, S., Tu, Y., & Dong, Y. (2022) · Advanced Theory and Simulations · doi:10.1002/adts.202200281 · Cited by 7
Quantum Computing in Civil Engineering: Potentials and Limitations
Ploennigs, J., Berger, M., Mevissen, M., & Smarsly, K. (2025) · Lecture Notes in Civil Engineering · doi:10.1007/978-3-031-87364-5_2 · Cited by 7
Solving Flexible Job-Shop Scheduling Problems Based on Quantum Computing
Fu, K., Liu, J., Chen, M., & Zhang, H. (2025) · Entropy · doi:10.3390/e27020189 · Cited by 6
“Quantum computers utilize quantum bits (qubits) as their fundamental units, which can exist simultaneously in superposition states of 0 and 1 [17].”
Discrete optimal control of building to grid integration based on quantum computing
Deng, Z., & Dong, B. (2023) · Building Simulation Conference proceedings · doi:10.26868/25222708.2023.1545 · Cited by 6
“…researchers have been working on developing algorithms and utilizing quantum annealers to address optimization problems, particularly challenging multivariable optimization (Mukherjee and Chakrabarti, 2015; Ajagekar et al, 2020) and network optimizations (Ding et al, 2021; Bhatia et al, 2020).”
BackgroundDiaz-Pier, S., & Carloni, P. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102817 · Cited by 6
Hybrid quantum-classical multi-cut Benders approach with a power system application
Paterakis, N. G. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108161 · Cited by 6
On Quantum Computing for Mixed-Integer ProgrammingPreprint
Chang, C.-Y., Jones, E. B., & Graf, P. A. (2020) · arXiv (Cornell University) · Cited by 6
Quantum Computing in Community Detection for Anti-Fraud Applications
Wang, Y., Yang, X., Ju, C., Zhang, Y., Zhang, J., Xu, Q., Wang, Y., Gao, X., Cao, X., Ma, Y., et al. (2024) · Entropy · doi:10.3390/e26121026 · Cited by 8
Decoherence dynamics estimation for superconducting gate-model quantum computers
Gyöngyösi, L. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-02863-7 · Cited by 6
A variant on the CREST iMTD algorithm for noncovalent clusters of flexible molecules
King, N. J., LeBlanc, I., & Brown, A. (2024) · Journal of Computational Chemistry · doi:10.1002/jcc.27458 · Cited by 4
Quantum gate control pulse optimization based on the Adam algorithm
Yang, M., Feng, Y., Lu, B., Zhao, H., Ma, G. L., & Wang, L. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04791-w · Cited by 4
“Constraints usually include physical limits on control parameters, such as bounds on amplitude and phase [22, 23].”
Development of Evolutionary Systems Based on Quantum Petri Nets
Letia, T. S., Durla-Pasca, E. M., Al-Janabi, D., & Cuibus, O. (2022) · Mathematics · doi:10.3390/math10234404 · Cited by 4
Distributed quantum computation for near-term quantum environments
Gyöngyösi, L., & Imre, S. (2021) · doi:10.1117/12.2585996 · Cited by 3
Hosamo, H., Plevris, V., Kraniotis, D., & Rolfsen, C. N. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116156 · Cited by 3
Quantum circuit for implementing AES S-box with low costs
Chen, H.-N., Cai, B., Gao, F., & Lin, S. (2026) · Quantum Information Processing · doi:10.1007/s11128-026-05083-7 · Cited by 5
“This grants quantum computing the potential to surpass classical computation, especially by offering significant speedup in solving certain complex problems [7], [8], [9].”
Quantum-Inspired Optimization for Enterprises
Thilagavathy, R., Gayathri, M. K., Sandhia, G. K., & Pushpalatha, M. (2024) · Advances in computer and electrical engineering book series · doi:10.4018/979-8-3693-1168-4.ch018 · Cited by 3
𝐶𝑙𝑎𝑠𝑠𝑖|𝑄⟩: Towards a Translation Framework to Bridge the Classical-Quantum Programming Gap
Esposito, M., Sabzevari, M. T., Ye, B., Falessi, D., Khan, A. A., & Taibi, D. (2024) · doi:10.1145/3663531.3664752 · Cited by 3
“Since Quantum Computing has reached the so-called quantum supremacy [20], the race to “ quantumize the algorithm ” is hastened by industry and research in every field [1, 2, 23, 25, 28, 31–33].”
Hybrid Quantum-Classical Computing via Dantzig-Wolfe Decomposition for Integer Linear Programming
Wei, X., Liu, J., Fan, L., Guo, Y., Han, Z., & Wang, Y. (2024) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn61486.2024.10637633 · Cited by 3
A multi-process parallel clustering algorithm for resource reconfiguration in cloud manufacturing
Liang, B., & Gao, J. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06607-7 · Cited by 3
“For example, Manufacturing Semantics Ontology [23, 25, 54] can be used to standardize data, improving algorithm efficiency to solve the problem of massive amounts of MR data [21] and using continuous optimization to adapt to the ever-changing manufacturing needs and environment [1].”
Quantum annealing for CAD-based disassembly sequence optimization
Azucena, J., & Prioli, J. P. J. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-17182-3 · Cited by 2
Saha, N., Bose, R., Roy, S., Sutradhar, S., Das, S., Khan, A. A., Ali, F., AlZubi, A. A., & Alanazi, J. M. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3597 · Cited by 2
Quantum annealing for remote sensing data processing: A review of optimization based applications
Misra, I., Moorthi, S. M., & Dhar, D. (2026) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2026.101936 · Cited by 2
Performance Optimization of Quantum Computing Applications using D Wave Two Quantum Computer
Sundar, G. N., & Narmadha, D. (2021) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei51242.2021.9452929 · Cited by 2
“Static power dissipation [5],[14] A current biased demultiplexer introduced to minimize the heat dissipation, in D Wave One.”
BackgroundAn Overview of Quantum Computing Approach in the Present-Day Energy Systems
Biswas, C., Pal, J., & Majumder, S. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-2412-5_3 · Cited by 2
Experimental study on the information disclosure problem: Branch-and-bound and QUBO solver
Otaki, K., Okada, A., & Yoshida, H. (2023) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2023.1150921 · Cited by 2
“d), we apply a technique [25, 26] tominimize”
Quantum Annealing for Real-World Machine Learning Applications
Nath, R. K., Thapliyal, H., & Humble, T. S. (2023) · Springer eBooks · doi:10.1007/978-3-031-37966-6_9 · Cited by 2
Self-citationQuantum Computing Algorithms for Nonlinear Optimization Problems
Vishwakarma, V. K. (2023) · Communications on Applied Nonlinear Analysis · doi:10.52783/cana.v30.279 · Cited by 3
Testing quantum and simulated annealers on the drone delivery packing problem
Tarquini, S., Dragoni, D., Vandelli, M., & Tudisco, F. (2026) · Quantum Machine Intelligence · doi:10.1007/s42484-026-00362-z · Cited by 4
“For example, in (Ajagekar et al., 2020) it is shown that the branch-and-cut algorithm implemented in Gurobi was unable to solve the considered optimization problem within 24 hours, and hence yielded a sub-optimal solution.”
Ajagekar, A., & You, F. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283034 · Cited by 2
“Quantum computing (QC) based applications have been gaining traction recently due to their unique capabilities with a significant portion of its presence perceived in the area of optimization with applications in molecular design [1, 2], process scheduling and operations [2-4], logistics”
BackgroundSelf-citationQuantum annealing computing for grid partition in large-scale power systems
Wang, D., Zheng, K., Chen, Q., Li, Z., & Liu, S. (2022) · IEEE International Electrical and Energy Conference (CIEEC) · doi:10.1109/cieec54735.2022.9846717 · Cited by 2
Quantum-Inspired Solvers on Mixed-Integer Linear Programming Problem
Wang, H., Pan, Y., & Cui, W. (2022) · Chinese Control Conference (CCC) · doi:10.23919/ccc55666.2022.9902772 · Cited by 2
“Fortunately, such puzzles have been able to be partitioned into solvable Ising problems combined with classical efforts[30].”
BackgroundAshok, P. C., Ragunthar, T., James, T. P. G., Sahayaraj, K. K. A., Suganthi, P., Somasundaram, K., & Ananthi, S. (2023) · Optical and Quantum Electronics · doi:10.1007/s11082-023-05154-4 · Cited by 2
RetractedModified Deep Residual Quantum Computing Optimization Technique for IoT Platform
El-Aziz, R. M. A., Rayan, A., Shahin, O. R., Elhadad, A., Abozeid, A., & Taloba, A. I. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0121244 · Cited by 2
Quantum Computing in Green Energy ProductionPreprint
Sharabiani, M. T. A., Jakobsen, V. B., Jeppesen, M., & Mahani, A. S. (2021) · arXiv (Cornell University) · Cited by 3
RETRACTED: Liquid Crystals: Unlocking the Quantum Revolution in ComputingPreprint
Otieno, E., Matczyszyn, K., & Mokaya, N. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-ql0fx · Cited by 1
RetractedExploring Quantum Optimization for Computer-aided Molecular and Process Design
Iftakher, A., & Hasan, M. M. F. (2024) · Systems and Control Transactions · doi:10.69997/sct.143809 · Cited by 1
Mappas, V. K., Dorneanu, B., Nolasco, E., Vassiliadis, V. S., & Arellano‐García, H. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2025.08.031 · Cited by 1
Quantum-Assisted Resource Management for Data Access in Space-Air-Ground Integrated Networks
Wei, X., Liu, J., Fan, L., Guo, Y., Gong, Y., Han, Z., & Wang, Y. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3616188 · Cited by 1
“Such hybrid quantum optimization has been newly applied in different areas including machine learning, mobile computing, network communication, and task scheduling [6], [7], [8], [9], [10], [11], [44], [45], [46], [47].”
Phishing threat mitigation in E-commerce using a quantum-enhanced hybrid AI framework
Gupta, B. B., Pan, V. S.-H., Gaurav, A., Arya, V., Attar, R. W., Alhazmi, A. H., Alhomoud, A., & Chui, K. T. (2025) · Scientific Reports · doi:10.1038/s41598-025-27515-7 · Cited by 1
Kül, S., Çeltek, S. A., & Balcı, S. (2025) · International Symposium on Advanced Topics in Electrical Engineering · doi:10.1109/atee66006.2025.11299906 · Cited by 1
“Early implementations show promising results for unit commitment and power flow optimization in hybrid energy systems [48].”
Mendoza-Fierro, J. A., Aguilar, L. M. A., & Otero, M. M. M. (2026) · Quantum Information Processing · doi:10.1007/s11128-025-05033-9 · Cited by 1
“The revolutionary results include optimization problems [2, 3], fast simulation of complex quantum systems [4, 5], and speed-up in integers factorization [6]; thus, QC promises to improve enormously all generation of incoming technologies which will lay the foundation of unprecedented advancement…”
Quantum computing in transport science: a review
Niu, C., Irannezhad, E., Myers, C. R., & Dixit, V. (2026) · Transportation Letters · doi:10.1080/19427867.2026.2640414 · Cited by 1
Nguyen, V. D., Kuci, E., Rasquin, M., & Noels, L. (2026) · Acta Mechanica Sinica · doi:10.1007/s10409-025-25157-x · Cited by 1
“Wils & Chen [32] considered a symbolic finite element approach to express the objective function under a fractional form whose minimisation follows an iterative procedure of minimising adaptive non-fractional functions which are subsequently transformed into a QUBO form [37].”
Fukushima, K., Ohno, H., & Fukushima, Y. (2026) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2026.06.032 · Cited by 1
An Integer Programming Model for the Optimal Qubit Allocation
Choi, H., & Choi, I.-C. (2021) · Journal of Korean Institute of Industrial Engineers · doi:10.7232/jkiie.2021.47.2.199 · Cited by 1
User Scheduling and Clustering for Distributed Antenna Network Using Quantum Computing
Hanakago, K., Takahashi, R., Ohyama, T., & Adachi, F. (2023) · IEICE Transactions on Communications · doi:10.1587/transcom.2023ebt0004 · Cited by 1
Osaba, E., Gelabert, G., Villar-Rodríguez, E., Asla, A., & Oregi, I. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3556-3_16 · Cited by 1
Rana, M. (2025) · International Conference on Computational Modelling, Simulation and Optimization (ICCMSO) · doi:10.1109/iccmso67468.2025.00082 · Cited by 1
“Operations planning [11], molecular design [12], process scheduling and operations, and logistics optimisation [13], and due to its unique capabilities, applications that utilise quantum computing (QC) have lately acquired prominence in a variety of sectors, including energy systems.”
Solving Graph Problems Using Permutation-Invariant Quantum Machine Learning
Mansky, M. B., Rohe, T., Stenzel, G., Sünkel, L., de la Serna, A. B., Castillo, S. L., Sathish, G., Nikolaidou, D., Bondarenko, D., Menzel, L., et al. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00178 · Cited by 1
Energy transfer and thermodynamics of quantum gravity computation
Gyöngyösi, L. (2020) · Chaos Solitons & Fractals X · doi:10.1016/j.csfx.2020.100050 · Cited by 1
Quantum Computers - An Emerging Cybersecurity Threat
Dubček, D. J. (2021) · Annals of Disaster Risk Sciences · doi:10.51381/adrs.v3i2.56 · Cited by 1
Fault Diagnosis of Electrical Power Systems with Hybrid Quantum-Classical Deep Learning
Ajagekar, A., & You, F. (2021) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-323-88506-5.50181-9 · Cited by 1
Self-citationA Hybrid Solution Approach for Large-scale Batch Scheduling with Quantum Computing
Ajagekar, A., & You, F. (2021) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-323-88506-5.50271-0 · Cited by 1
Self-citationApproximation Method for Optimization Problems in Gate-Model Quantum Computers
Gyöngyösi, L. (2021) · Chaos Solitons & Fractals X · doi:10.1016/j.csfx.2021.100066 · Cited by 1
Benchmarks and Controls for Optimization with Quantum Annealing
Grant, E. (2020)
Resource optimization for the quantum Internet
Gyöngyösi, L., & Imre, S. (2022) · Quantum Computing, Communication, and Simulation · doi:10.1117/12.2607957
“INTRODUCTION The quantum Internet aims to provide an adequate answer to the computational power that will become available with quantum computers [1-28], [25,29-64].”
BackgroundModern Approaches to Topological Quantum Error Correction
Parrado-Rodríguez, P. (2022) · Cronfa (Swansea University) · doi:10.23889/suthesis.60302
OFFICERS: Operational Framework for Intelligent Crime-and-Emergency Response Scheduling
Chase, J., Goh, S. T., Phong, T., & Lau, H. C. (2022) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v32i1.19830
“Due to the scale limitations of quantum devices, hybrid methods or decomposition have been proposed (Ajagekar, Humble, and You 2020), (Stollenwerk, Lobe, and Jung 2019).”
MethodA Variant on the CREST Algorithm for Non-Covalent Clusters of Flexible MoleculesPreprint
King, N. J., LeBlanc, I., & Brown, A. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-hvs2p
Optimizing Product Influence of Shelf DisplayPreprint
Chaimanowong, W., Cisternas, F., & Neira, D. E. B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4821663
Preparing Students to Master Hybrid and Co-Processing Methods for High Performance Computing
Siewert, S. B. (2024) · ASEE Pacific Southwest Section Conference (PSW) · doi:10.18260/1-2--46055
Scheduling algorithm for fidelity of quantum gate circuits
Liu, X., Xu, J., & Lian, H. (2024) · International Conference on Electronic Information Engineering and Data Processing (EIEDP) · doi:10.1117/12.3032999
“Several institutions have released operating systems [7] for quantum computers to effectively manage quantum computing devices [8] and efficiently utilize quantum computing resources [9] .”
Reverse design of molecule‐process‐process networks: A case study from HEN ‐ ORC system
Hong, X., Dong, X., Liao, Z., Sun, J., Wang, J., & Yang, Y. (2024) · AIChE Journal · doi:10.1002/aic.18643
Phases Unlocked: The Crucial Role of Qubit Phases in Quantum Computing
Jaradat, Y. M., Masoud, M. Z., MANSRAH, A., Jannoud, I., & Alia, M. A. (2024) · The Eurasia Proceedings of Science Technology Engineering and Mathematics · doi:10.55549/epstem.1604685
QUBO-Net: A Reverse Optimization Framework via Quantum Computing
Shen, D., Li, J., Zha, S., Cao, Y., Zhang, S., & Yang, Y. (2024) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai65059.2024.10893730
“In parallel, quantum computing has emerged as a transfor-mative technology capable of addressing complex problems more efficiently than classical methods [4].”
Yu, S., Deng, Y., Kang, C., & Wang, X. (2025) · Highlights in Science Engineering and Technology · doi:10.54097/mjrntp76
“As quantum computing advances, it holds great promise for transforming industries that rely on complex optimization tasks, such as logistics, finance, and smart manufacturing [5,6] .”
Quantum Computing for Supply Chain and Logistics Optimization The Evolution of Computing Technology
Jaiswal, C. (2023) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit239076
Co-operative Decision-making: The Balancing Act of Risk and Safety in the Modern Era
Ghosh, S. (2025) · Splint International Journal Of Professionals · doi:10.5958/2583-3561.2025.00012.2
Fuzzy Gaussian matheuristics for aggregate production planning with fuzzy stochastic demand
Hoang, H.-Y., & Nguyen, V.-H. (2025) · Journal of Industrial and Production Engineering · doi:10.1080/21681015.2025.2536318
“…Competitive, Xu et al. [26]; etc.) to different physics-based methods (Simulated Annealing; Quantum-Inspired Optimizations, Ajagekar et al. [27]; Ganesan et al. [28]; etc.), to swarm intelligence techniques (Particle Swarm Optimization, Goat Search, De [29]; and Ouster Collection, De and…”
Quantum Computing for Supply Chain Management and Logistics
Soni, U., Kumar, A., Bansal, S., & Sharma, S. B. (2025) · International Conference on Technology Enabled Economic Changes (InTech) · doi:10.1109/intech64186.2025.11198261
“Thus, the QAOA and QUBO formulations are combined in the right proportions in order to assist, rather than replace classical computing [6], [11], [17], [20], [22].”
Yusuff, T. A., Iloeje, K. F., Peteranaba, S. F., Akinlolu, V. S., Kassim, N., & Hamidu, Z. (2025) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v4i10.921
Quantum-Enhanced Plant Breeding and Genetic Optimization
Kaur, P., Jasrai, L., & Sandhu, R. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3957-3.ch005
Distributed quantum generalized benders decomposition for unit commitment problems
Gao, F., Huang, D., Zhao, Z., Dai, W., Yang, M., Gao, Q., & Pan, Y. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04977-2 · Cited by 3
A Quantum Search Method for Quadratic and Multidimensional Knapsack Problems
Wilkening, S., Lefterovici, A.-I., Binkowski, L., Funck, M., Perk, M., Karimov, R., Fekete, S. P., & Osborne, T. J. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00241
“Combinatorial optimization problems serve as a focal point for this research [4], [5], offering diverse applications.”
Du, Z., Wandelt, S., & Sun, X. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105505
Hyper-Automated AIOps for Unified Operations Orchestration in Industry
Rana, M., Giri, N., & Gadde, S. S. (2025) · International Conference on Artificial Intelligence for Sustainable Innovation (AI-SI) · doi:10.1109/ai-si66213.2025.11340854
“Organizations still confront many important difficulties notwithstanding the explosion of monitoring tools and growing acceptance of AIOps [12]: Context deficit in traditional monitoring systems; manual remedial processes, with 68% of companies still mostly depending on human intervention [11];…”
Quantum Computing Applications Across Industrial Sectors
Maddala, V. S. P. (2026) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.4835
“…quantum computing for solving large-scale optimization problems is based on decomposition methods, which divide complicated industrial problems into smaller quantum-computational versions assisted by classical optimization methods for handling coordination among different decomposed solutions [3].”
Quantum-Enhanced Modeling and Optimization of Sodium-Ion Batteries
Masemola, K., Nyamupangedengu, C., & Dorrell, D. G. (2025) · SPEC Workshops · doi:10.1109/spec64875.2025.11376854
“Demonstrations, such as those with D-Wave (16 qubit) processors, have shown performance advantages over classical solvers like simulated annealing in tackling (nondeterministic polynomial-) NP-hard graph problems [31].”
Drone Delivery Packing Problem on a Neutral‐Atom Quantum Computer
Tarquini, S., Vandelli, M., Ferrari, F., Dragoni, D., & Tudisco, F. (2026) · Advanced Quantum Technologies · doi:10.1002/qute.70468
“Many problems in this class can be encoded in a form suitable for quantum computers [2, 3], enabling the use of quantum or hybrid quantum-classical solvers [4].”
Soy, A., Kizi, K. M. A., Bhuvaneshwari, K. S., F, M. H., Thakur, D., & Priyanka, T. P. (2025) · International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) · doi:10.1109/ietacs68750.2025.11385608
“Though these hybrid models show a theoretical improvement, they leave open research problems of integrating quantum components into existing NLP pipelines while guaranteeing scalability concerning various hardware parameters [9].”
Yapı Mühendisliğinde Kuantum Hesaplama: Gelecek Uygulamalar İçin Teorik Bir Çerçeve
Kolcu, F. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1267.c5161
Yiğitcanlar, T., Hossain, S. T., Shaamala, A., & Li, W. (2026) · Preprints.org · doi:10.2139/ssrn.6571118
Quantum Computing for Scheduling Problems: A Survey
Ammar, S. B., Marzouki, B., & Driss, O. B. (2025) · IEEE Afro-Mediterranean Conference on Artificial Intelligence (AMCAI) · doi:10.1109/amcai66110.2025.11474388
“Using commercially available devices, recent research has investigated variational quantum algorithms, gate-based algorithms, and quantum annealing in both theoretical and real-world case studies [5], [6].”
Quantum-Classical Hybrid Algorithm Using Quantum Annealing for Multi-Objective Job Shop Scheduling
Sawamura, K., Araki, K., Maruyama, N., Haba, R., & Ohzeki, M. (2026) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.95.054002 · Cited by 7
Task Allocation on a Quantum Computer for a Two-Machine Manufacturing Cell
Bożejko, W., Trotskyi, S., Uchroński, M., & Wodecki, M. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19763-4_29
Solving mixed-integer problems as QUBO: Encodings, reformulations, and rolling-precision algorithm
Iftakher, A., Türkay, M., & Hasan, M. M. F. (2026) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2026.109757
Tightiz, L., Dang, L. M., & Yang, H.-S. (2026) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2026.105312
Quantum Paradigm in Energy Systems: Architecture of the "Quantum-Nuclear Nexus" System
Gajinović, B. V., Ralević, N., Dumnic, B. P., & Đaković, V. (2026) · WSEAS TRANSACTIONS on SYSTEMS archive · doi:10.37394/23202.2026.25.46
A synthetic-data and machine-learning digital shadow foundation for end-of-life magnet recycling
Sifakis, N., Chachalis, A., Tsinarakis, G., Aryblia, M., Cholidis, D., & Arampatzis, G. (2026) · Measurement Digitalization · doi:10.1016/j.meadig.2026.100065
Metadata
- Authors
- Ajagekar, A., Humble, T., & You, F.
- Affiliations
- Cornell University (US)
- Oak Ridge National Laboratory (US)
- Corresponding author
- You, Fengqi
- Venue
- Computers & Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 132
- Pages
- 106630
- ISSN
- 0098-1354 (ISSN-L)
- OpenAlex
- W2982469373
- Cited by
- 191
- References
- 91
- Citations by year
- 2026: 19 · 2025: 37 · 2024: 35 · 2023: 28 · 2022: 30 · 2021: 25 · 2020: 17
- FWCI
- 8.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computers & Chemical Engineering (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Energy (AC05-00OR22725, DE--AC05--00OR22725, DE-AC05?, 00OR22725)
- Office of Science (DEAC05- 00OR22725, AC05-00OR22725)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- quantum computing, hybrid quantum-classical algorithms, mixed integer programming, large-scale optimization, molecular conformation, job shop scheduling, manufacturing cell formation, vehicle routing, combinatorial optimization
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