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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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“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].”
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“Current commercial quantum devices impose some inherent limitations, such as noise or decoherence [7].”
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Quantum circuit representation of Bayesian networks
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Quantum Computing Opportunities in Renewable Energy
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Optimality guarantees for crystal structure prediction
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“These types of computationally challenging tasks could be offloaded to quantum processors for optimization [4].”
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“Quantum annealing has been demonstrated for its advantages and speedups in solving a variety of combinatorial optimization problems [34]–[38].”
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NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems
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“, 2021) and hybrid algorithms (Ajagekar et al., 2020; Callison and Chancellor, 2022) but these are beyond the scope of our work.”
BackgroundForecasting Bike Sharing Demand Using Quantum Bayesian Network
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Quantum Computing Optimization Technique for IoT Platform using Modified Deep Residual Approach
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“Recently, quantum computing has gained momentum with applications in safety-critical domains such as traffic flow [18], aircraft load [38], logistics [2], and medical diagnostics [21].”
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Molecular design with automated quantum computing-based deep learning and optimization
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Self-citationQuantum Computing in Supply Chain Management State of the Art and Research Directions
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Hybrid quantum annealing via molecular dynamics
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Quantum State Optimization and Computational Pathway Evaluation for Gate-Model Quantum Computers
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“ion. Further performance analyses of the QAOA algorithm can be found in [83,84]. Practical implementations connected to gate-model quantum computing and the QAOA algorithm can be found in [85,86]. In [41], the authors studied methods quantum computing based hybrid solution methods for large-scale discrete-continuous o…”
BackgroundMethodEntanglement accessibility measures for the quantum Internet
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“As quantum computers evolves significantly [1–10], there arises a fundamental need for a communication network that provides unconditionally secure communication and all the network functions of the traditional internet.”
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“A programmer can use this tangled knowledge to build and troubleshoot algorithms [15, 17, 49].”
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“The D-Wave annealers used in [8,9] are explicitly built for optimization and sampling problems; therefore, optimization problems tend to run faster on them than on classical computers.”
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Routing space exploration for scalable routing in the quantum Internet
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A scientometric analysis of quantum driven innovations in intelligent transportation systems
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Distance-based clustering using QUBO formulations
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“The iterative fractional-cost method originates from the hybrid parametric method proposed to solve the vehicle routing problem 24 .”
MethodMoraglio, A., Georgescu, S. P., & Sadowski, P. (2022) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3520304.3533965 · Cited by 23
“Casting a combinatorial optimisation problem in QUBO form has been the key to achieving great speedups in large-scale optimisation problems ranging from material simulations and molecular design to logistics and scheduling [1, 2, 15].”
BackgroundWei, X., Fan, L., Guo, Y., Gong, Y., Han, Z., & Wang, Y. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3440930 · Cited by 28
“To that end, several hybrid quantum-classical solutions [16], [17] have been proposed to tackle optimization problems by leveraging the complementary strengths of quantum and classical computers.”
Dynamics of entangled networks of the quantum Internet
Gyöngyösi, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-68498-x · Cited by 23
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“The authors in [21] and [22] discretize continuous variables into h parts to turn them into binary variables.”
BackgroundQuantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study
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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].”
BackgroundMethodUnsupervised Quantum Gate Control for Gate-Model Quantum Computers
Gyöngyösi, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-67018-1 · Cited by 19
“Objective function examples can be found in 10,11 .”
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“This paper is focused on the second of these categories, optimization, for which QC provides revolutionary mechanisms for facing complex problems with a remarkable advantage [3].”
BackgroundOptimizing the optimizer: decomposition techniques for quantum annealing
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A Symbolic Approach to Discrete Structural Optimization Using Quantum Annealing
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“developed a method with which the optimum solution to fractional objective functions can be found, by iteratively solving an adaptive non-fractional function [28].”
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“The quantum annealer, through the continuous evolution of the quantum system, searches for the minimum energy of the Ising Hamiltonian (see Ajagekar et al., 2020; Denkena et al., 2021).”
Hybrid quantum-classical heuristic for the bin packing problem
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“In a nutshell, QC provides a revolutionary approach for dealing with complex problems with a possible significant advantage [2].”
BackgroundQuantum Computing and Resilient Design Perspectives for Cybersecurity of Feedback Systems
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“Recently, quantum computing has been applied to problems in process systems engineering such as optimization and machine learning (Ajagekar et al. (2020); Ajagekar and You (2019)).”
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“Hence, cloud TS falls into the category of NP-hard problems [6, 7].”
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Theory of Noise-Scaled Stability Bounds and Entanglement Rate Maximization in the Quantum Internet
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“The connections in the quantum Internet are formulated by a set of quantum repeaters and the legal parties have access to large-scale quantum devices [13–17] such as quantum computers [18–29].”
BackgroundQuantum Path Integral Approach for Vehicle Routing Optimization With Limited Qubit
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“Quantum approximation optimization approaches are a subset of the class of hybrid quantum-classical variational algorithms that use the annealing technique [38], [39].”
BackgroundMethodEntanglement From Sky: Optimizing Satellite-Based Entanglement Distribution for Quantum Networks
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“Such hybrid quantum optimization has been applied in different areas including machine learning, mobile computing, network communication, and task scheduling [20], [21], [22], [23], [24], [25], [26], [27], [28].”
Resource prioritization and balancing for the quantum internet
Gyöngyösi, L., & Imre, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-78960-5 · Cited by 16
A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research
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When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
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“Recent studies demonstrated that hybrid models that use this technique can outperform fully classical models in specific tasks such as quantum chemistry simulations and optimization problems [14], [83].”
Optimization of a refinery scheduling process with column generation and a quantum annealer
Ossorio-Castillo, J., & Pena-Brage, F. (2021) · Optimization and Engineering · doi:10.1007/s11081-021-09662-8 · Cited by 14
“Other hybrid methods that exploit the complementary strengths of quantum and classical computers can be consulted in Ajagekar et al. (2020) and Ajagekar (2020).”
BackgroundAutoQUBO v2: Towards Efficient and Effective QUBO Formulations for Ising Machines
Pauckert, J., Ayodele, M., García, M. D., Georgescu, S. P., & Parizy, M. (2023) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3583133.3590662 · Cited by 14
“They were shown to provide significant speed-ups for certain combinatorial optimization problems [1, 2].”
BackgroundControl Implemented on Quantum Computers: Effects of Noise, Nondeterminism, and Entanglement
Nieman, K., Rangan, K. K., & Durand, H. (2022) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.2c00454 · Cited by 14
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Quantum computing-enhanced large-scale residential electric vehicle charging management
Deng, Z., Li, Y., Wang, X., Jiang, Z., & Dong, B. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126772 · Cited by 13
Quantum-Enhanced Machine Learning
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“…that the field is still in its early stages, and further research is needed to explore the full potential of quantum computing in addressing the optimization challenges faced by manufacturing and supply chain industries (Ajagekar & You, 2019)(Bayerstadler et al., 2021)(Ajagekar et al., 2020).”
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“Ajagekar et al. apply a decomposition of the problem into a constrained MILP problem and unconstrained QUBO sub-problems that would be solved by quantum annealer [45].”
BackgroundTransit facility allocation: Hybrid quantum-classical optimization
Gabbassov, E. (2022) · PLoS ONE · doi:10.1371/journal.pone.0274632 · Cited by 12
Quantum Assisted Scheduling Algorithm for Federated Learning in Distributed Networks
Wei, X., Fan, L., Guo, Y., Gong, Y., Han, Z., & Wang, Y. (2023) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn58024.2023.10230094 · Cited by 14
“To that end, several hybrid quantum-classical solutions [16], [17] have been proposed to tackle optimization problems by leveraging the complementary strengths of quantum and classical computers.”
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Matheuristics for mixed-model assembly line balancing problem with fuzzy stochastic processing time
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Quantum computing for several AGV scheduling models
Tang, L. B., Yang, C., Wen, K., Wu, W., & Guo, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-62821-6 · Cited by 12
“Quantum computers utilize quantum bits (qubits) as the fundamental units of information storage 8 , which can exist in superposition states of both 1 and 0, enabling them to hold exponentially more information compared to traditional computers.”
BackgroundAdaptive Problem Solving Dynamics in Gate-Model Quantum Computers
Gyöngyösi, L. (2022) · Entropy · doi:10.3390/e24091196 · Cited by 12
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
“Hybrid pipelines that combine both quantum and classical approaches are especially beneficial for solving complex optimization problems [10, 11].”
BackgroundObjective function estimation for solving optimization problems in gate-model quantum computers
Gyöngyösi, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-71007-9 · Cited by 11
“Objective function examples can be found in 9,24 .).”
BackgroundEntanglement concentration service for the quantum Internet
Gyöngyösi, L., & Imre, S. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-02716-3 · Cited by 11
Quantum computing and supply chain optimization: addressing complexity and efficiency challenges
Hwang, M.-L. T., Collin, W.-L., & Wang-xu, L. S. (2021) · International Journal of Enterprise Modelling · doi:10.35335/emod.v15i3.49 · Cited by 14
“Quantum computing has the potential to revolutionize conventional supply chain management strategies(Cacciapuoti et al., 2019)(Ajagekar et al., 2020)(Hanelt et al., 2021), offering the promise of more efficient network designs, improved inventory management, enhanced demand forecasting, and robust…”
BackgroundHybrid Quantum Tabu Search for Solving the Vehicle Routing Problem
Holliday, J. B., Morgan, B., Churchill, H., & Luu, K. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.10305 · Cited by 10
“Lastly, an interesting cost function was defined in [18], where a ratio between distance and work time was used to define the cost.”
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
“Venturelli et al. (2016), Ajagekar et al. (2020) (b), Ajagekar et al. (2022), Carugno et al. (2022), Yonaga et al. (2022), Aggoune and Deleplanque (2023) and Pérez Armas et al. (2024) use a QASA to resolve a JSSP. Schworm et al. (2023b) propose a QASA for dynamic JSSP.”
Review of the application of quantum annealing-related technologies in transportation optimization
Mohammed, M. Q., Meeß, H., & Otte, M. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04870-y · Cited by 12
Deng, Z., Wang, X., & Dong, B. (2025) · Building Simulation · doi:10.1007/s12273-025-1248-4 · Cited by 9
“Recently, researchers are focusing on algorithms and quantum annealers for tackling difficult multivariable optimization problems (Mukherjee and Chakrabarti 2015; Ajagekar et al. 2020) and network optimizations (Bhatia and Sood 2020; Ding et al. 2021).”
Hybrid divide-and-conquer approach for tree search algorithms
Rennela, M., Brand, S., Laarman, A., & Dunjko, V. (2023) · Quantum · doi:10.22331/q-2023-03-23-959 · Cited by 9
“see [1]) fit in this paradigm, and they ostensibly ‘get more’ out of the device, however mostly in a heuristic setting where little can be proved.”
BackgroundJami, A. R., & Haleem, A. (2025) · Human Settlements and Sustainability · doi:10.1016/j.hssust.2025.05.005 · Cited by 11
Lv, B. (2025) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2025.104458 · Cited by 8
Quantum Computing in Transportation Engineering: A Survey
Somvanshi, S., Islam, M. M., Polock, S. B. B., Chhetri, G., Anderson, D., Anandi, D., & Das, S. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3662626 · Cited by 8
“…freeway sensors) for tra ffi c prediction, alongside baselines such as OR-Tools LNS, MILP, max-pressure control, reinforcement learning (e.g., Deep Q-Networks and Proximal Policy Optimization), and graph-based neural networks such as STGCN or Long Short-Term Memory (LSTM) models [10], [34].”
InfluentialPost-processing optimization for continuous-variable quantum key distribution
Gyöngyösi, L. (2021) · Theoretical Computer Science · doi:10.1016/j.tcs.2021.08.023 · Cited by 8
A quantum algorithm for solving 0-1 Knapsack problems
Wilkening, S., Lefterovici, A.-I., Binkowski, L., Perk, M., Fekete, S. P., & Osborne, T. J. (2025) · npj Quantum Information · doi:10.1038/s41534-025-01097-8 · Cited by 8
“In recent times, there has indeed been considerable optimism thatcombinatorial optimisationproblemsprovide a rich class of application areas whose solutions quantum computers will improve 17 .”
A study on lung cancer using nabla discrete fractional-order model
Amilo, D., Kaymakamzade, B., & Hınçal, E. (2023) · Mathematica Moravica · doi:10.5937/matmor2302055a · Cited by 7
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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