Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

Ajagekar, Akshay; Humble, Travis; You, Fengqi

doi:10.1016/j.compchemeng.2019.106630

Cited by 191Quantum Computing Algorithms and ArchitectureAdvanced Optimization Algorithms ResearchMetaheuristic Optimization Algorithms Research

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

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Ajagekar, Akshay, Humble, Travis, You, Fengqi·2020·Computers & Chemical Engineering

DOI 10.1016/j.compchemeng.2019.106630

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Ajagekar et al. 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.
“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]
The Conclusions (p. 42) identify all four problems and report a tailored hybrid quantum-computing method for each. But the paper calls only the job-shop scheduling and manufacturing cell formation methods decomposition methods; it describes the molecular conformation method as partitioning and the vehicle routing method as parametric. Calling all four “hybrid decomposition techniques” overstates the source. (LLM, conf=0.88)
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“Mapping the objective function onto the physical quantum processing unit is followed by the realization of quantum annealing process [35] which searches for low-energy solutions of the corresponding problem Hamiltonian [36].”
Self-citation
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Borchers, B., & Crawford, D. (1993) · Cited by 22 · see citing works
Identifying the minor set cover of dense connected bipartite graphs via random matching edge sets
Hamilton, K. E., & Humble, T. S. (2017) · Quantum Information Processing · doi:10.1007/s11128-016-1513-7 · Cited by 19 · cited 1× · see citing works
“Since the Chimera lattice is not fully connected, mapping of variables to physical qubits uses the process of minor-embedding and is crucial to problem solving [30-33].”
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A discrete-continuous algorithm for molecular energy minimization
Maier, R. S., Rosen, J. B., & Xue, G. (1992) · doi:10.5555/147877.148130 · Cited by 15 · see citing works
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Background
Quantum Realism
Humble, T. S., & DeBenedictis, E. P. (2019) · Computer · doi:10.1109/mc.2019.2908512 · Cited by 11 · cited 1× · see citing works
“Therefore, harnessing the complementary strengths of classical and quantum computers to solve complex large-scale optimization problems has become the main strategy for near-term and mid-term solution [18, 19].”
BackgroundSelf-citation
QUBO formulation for the contact map overlap problem
de Oliveira, N. M., Silva, R. M. D. A., & de Oliveira, W. R. (2018) · International Journal of Quantum Information · doi:10.1142/s0219749918400075 · Cited by 7 · cited 1× · see citing works
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Quantum Circuit Designs for Gate-Model Quantum Computer Architectures.Preprint
Gyöngyösi, L. (2018) · arXiv (Cornell University) · Cited by 5 · see citing works

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“Current commercial quantum devices impose some inherent limitations, such as noise or decoherence [7].”
Background
Advanced food contaminant detection through multi-source data fusion: Strategies, applications, and future perspectives
Adade, S. Y. S., Lin, H., Johnson, N. A. N., Nunekpeku, X., Aheto, J. H., Ekumah, J., Kwadzokpui, B. A., Teye, E., Ahmad, W., & Chen, Q. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104851 · Cited by 74 · see citing works
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Circuit Depth Reduction for Gate-Model Quantum Computers
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Quantum circuit representation of Bayesian networks
Borujeni, S. E., Nannapaneni, S., Nguyen, N. H., Behrman, E., & Steck, J. E. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114768 · Cited by 63 · see citing works
Quantum Computing Opportunities in Renewable Energy
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Implementation of Quantum Annealing: A Systematic Review
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Quantum computing for future real-time building HVAC controls
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Optimality guarantees for crystal structure prediction
Gusev, V. V., Adamson, D., Deligkas, A., Antypov, D., Collins, C. M., Krysta, P., Potapov, I., Darling, G. R., Dyer, M. S., Spirakis, P. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06071-y · Cited by 54 · see citing works
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“These types of computationally challenging tasks could be offloaded to quantum processors for optimization [4].”
BackgroundSelf-citation
Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling
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Perspectives of quantum computing for chemical engineering
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Quantum Topology Optimization via Quantum Annealing
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“Quantum annealing has been demonstrated for its advantages and speedups in solving a variety of combinatorial optimization problems [34]–[38].”
Background
Quantum Artificial Intelligence: A Brief Survey
Klusch, M., Lässig, J., Müssig, D., Macaluso, A., & Wilhelm, F. K. (2024) · Künstliche Intell. · doi:10.1007/s13218-024-00871-8 · Cited by 52 · see citing works
“In [6, 8], an iterative quantum-supported hybrid MILP (Mixed Integer Linear Programming)-based optimal solu-tion of the JSS problem is decomposed in a way that is suitable for hybrid quantum-classical computing.”
NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems
Au-Yeung, R., Chancellor, N., & Halffmann, P. (2023) · Frontiers in Quantum Science and Technology · doi:10.3389/frqst.2023.1128576 · Cited by 46 · see citing works
“, 2021) and hybrid algorithms (Ajagekar et al., 2020; Callison and Chancellor, 2022) but these are beyond the scope of our work.”
Background
Forecasting Bike Sharing Demand Using Quantum Bayesian Network
Harikrishnakumar, R., & Nannapaneni, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119749 · Cited by 45 · see citing works
Quantum Computing Optimization Technique for IoT Platform using Modified Deep Residual Approach
El-Aziz, R. M. A., Taloba, A. I., & AlGhamdi, F. (2022) · Alexandria Engineering Journal · doi:10.1016/j.aej.2022.06.029 · Cited by 41 · see citing works
symQV: Automated Symbolic Verification of Quantum Programs
Bauer-Marquart, F., Leue, S., & Schilling, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-27481-7_12 · Cited by 38 · see citing works
“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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Forest 4.0: Technologies and digitalization to create the residual biomass supply chain of the future
Bastos, T., Teixeira, L. C., & Nunes, L. J. R. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.143041 · Cited by 39 · see citing works
Molecular design with automated quantum computing-based deep learning and optimization
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Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization
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New frontiers of quantum computing in chemical engineering
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Quantum Computing in Supply Chain Management State of the Art and Research Directions
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Variational quantum circuit based demand response in buildings leveraging a hybrid quantum-classical strategy
Ajagekar, A., & You, F. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123244 · Cited by 34 · see citing works
Self-citation
Untitled work
(2021) · doi:10.1109/cec45853.2021.9504923 · Cited by 40 · see citing works
Quantum computing and materials science: A practical guide to applying quantum annealing to the configurational analysis of materials
Camino, B., Buckeridge, J., Warburton, P., Kendon, V., & Woodley, S. (2023) · Journal of Applied Physics · doi:10.1063/5.0151346 · Cited by 32 · see citing works
Hybrid quantum annealing via molecular dynamics
Irie, H., Liang, H., Doi, T., Gongyo, S., & Hatsuda, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-87676-z · Cited by 32 · see citing works
Quantum State Optimization and Computational Pathway Evaluation for Gate-Model Quantum Computers
Gyöngyösi, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-61316-4 · Cited by 32 · see citing works
“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…”
BackgroundMethod
Entanglement accessibility measures for the quantum Internet
Gyöngyösi, L., & Imre, S. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-2605-y · Cited by 30 · see citing works
“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.”
Background
Quantum software engineering and quantum software development lifecycle: a survey
Dwivedi, K., Haghparast, M., & Mikkonen, T. (2024) · Cluster Computing · doi:10.1007/s10586-024-04362-1 · Cited by 30 · see citing works
“A programmer can use this tangled knowledge to build and troubleshoot algorithms [15, 17, 49].”
Integration of planning, scheduling, and control: A review and new perspectives
Andrés‐Martínez, O., & Ricardez‐Sandoval, L. (2022) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.24501 · Cited by 28 · see citing works
Quantum Computing as a Game Changer on the Path towards a Net-Zero Economy: A Review of the Main Challenges in the Energy Domain
Celsi, M. R., & Celsi, L. R. (2024) · Energies · doi:10.3390/en17051039 · Cited by 27 · see citing works
“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.”
Background
A novel quantum-inspired solution for high-performance energy-efficient data acquisition from IoT networks
Bhatia, M., Sood, S. K., & Sood, V. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02494-x · Cited by 27 · see citing works
Routing space exploration for scalable routing in the quantum Internet
Gyöngyösi, L., & Imre, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-68354-y · Cited by 28 · see citing works
Quantum annealing-assisted lattice optimization
Xu, Z., Shang, W., Kim, S., Lee, E., & Luo, T. (2025) · npj Computational Materials · doi:10.1038/s41524-024-01505-1 · Cited by 26 · see citing works
“Compared with quantum algorithms basedonuniversalgate-basedquantumhardwareinthenoisyintermediate-scale quantum (NISQ) era 41 , QA have become a viable tool for practically solvingdiscreteoptimizationproblemsinmaterialsciencedomain 42,43 .”
A scientometric analysis of quantum driven innovations in intelligent transportation systems
Monika, & Sood, S. K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109258 · Cited by 27 · see citing works
Distance-based clustering using QUBO formulations
Matsumoto, N., Hamakawa, Y., Tatsumura, K., & Kudo, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-06559-z · Cited by 24 · see citing works
“The iterative fractional-cost method originates from the hybrid parametric method proposed to solve the vehicle routing problem 24 .”
Method
AutoQubo
Moraglio, A., Georgescu, S. P., & Sadowski, P. (2022) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3520304.3533965 · Cited by 23 · see citing works
“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].”
Background
Hybrid Quantum–Classical Benders' Decomposition for Federated Learning Scheduling in Distributed Networks
Wei, 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 23 · see citing works
“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 · see citing works
Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling
Mahroo, R., & Kargarian, A. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3320872 · Cited by 22 · see citing works
“The authors in [21] and [22] discretize continuous variables into h parts to turn them into binary variables.”
Background
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
“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].”
BackgroundMethod
Unsupervised Quantum Gate Control for Gate-Model Quantum Computers
Gyöngyösi, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-67018-1 · Cited by 19 · see citing works
“Objective function examples can be found in 10,11 .”
Background
Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations
Villar-Rodriguez, E., Osaba, E., & Oregi, I. (2022) · IEEE Symposium Series on Computational Intelligence · doi:10.1109/ssci51031.2022.10022300 · Cited by 18 · see citing works
“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].”
Background
Optimizing the optimizer: decomposition techniques for quantum annealing
Bass, G., Henderson, M., Heath, J., & Dulny, J. S. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00039-9 · Cited by 21 · see citing works
A Symbolic Approach to Discrete Structural Optimization Using Quantum Annealing
Wils, K., & Chen, B. (2023) · Mathematics · doi:10.3390/math11163451 · Cited by 20 · see citing works
“developed a method with which the optimum solution to fractional objective functions can be found, by iteratively solving an adaptive non-fractional function [28].”
BackgroundMethod
Optimal solving of a binary knapsack problem on a D-Wave quantum machine and its implementation in production systems
Bożejko, W., Burduk, A., Pempera, J., Uchroński, M., & Wodecki, M. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06025-1 · Cited by 19 · see citing works
“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
de Andoin, M. G., Osaba, E., Oregi, I., Villar-Rodríguez, E., & Sanz, M. (2022) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3520304.3533986 · Cited by 18 · see citing works
“In a nutshell, QC provides a revolutionary approach for dealing with complex problems with a possible signiﬁcant advantage [2].”
Background
Quantum Computing and Resilient Design Perspectives for Cybersecurity of Feedback Systems
Rangan, K. K., Halloun, J. A., Oyama, H., Cherney, S., Assoumani, I. A., Jairazbhoy, N., Durand, H., & Ng, S. K. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.07.526 · Cited by 18 · see citing works
“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)).”
Method
Quantum annealing-driven branch and bound for the single machine total weighted number of tardy jobs scheduling problem
Bożejko, W., Pempera, J., Uchroński, M., & Wodecki, M. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.02.016 · Cited by 18 · see citing works
A novel hybrid Artificial Gorilla Troops Optimizer with Honey Badger Algorithm for solving cloud scheduling problem
Hussien, A. G., Chhabra, A., Hashim, F. A., & Pop, A. (2024) · Cluster Computing · doi:10.1007/s10586-024-04605-1 · Cited by 18 · see citing works
“Hence, cloud TS falls into the category of NP-hard problems [6, 7].”
Networked Quantum Services†
Gyöngyösi, L., & Imre, S. (2025) · Quantum Information and Computation · doi:10.2478/qic-2025-0006 · Cited by 18 · see citing works
Theory of Noise-Scaled Stability Bounds and Entanglement Rate Maximization in the Quantum Internet
Gyöngyösi, L., & Imre, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-58200-6 · Cited by 17 · see citing works
“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].”
Background
Quantum Path Integral Approach for Vehicle Routing Optimization With Limited Qubit
Gautam, K., & Ahn, C. W. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3327157 · Cited by 19 · see citing works
“Quantum approximation optimization approaches are a subset of the class of hybrid quantum-classical variational algorithms that use the annealing technique [38], [39].”
BackgroundMethod
Entanglement From Sky: Optimizing Satellite-Based Entanglement Distribution for Quantum Networks
Wei, X., Fan, L., Guo, Y., Han, Z., & Wang, Y. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3456789 · Cited by 16 · see citing works
“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 · see citing works
A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research
Gomes, C., Fernandes, J. P., Falcão, G., Kar, S., & TAYUR, S. R. (2024) · ACM Computing Surveys · doi:10.1145/3700874 · Cited by 15 · see citing works
Unlocking the potential of quantum computing in prefabricated construction supply chains: Current trends, challenges, and future directions
Chen, Z., Tan, Y. C., Ma, Z., Zhu, Z., & Skibniewski, M. J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103043 · Cited by 16 · see citing works
Focusing on the hybrid quantum computing - Tabu search algorithm: new results on the Asymmetric Salesman Problem
Osaba, E., Villar-Rodriguez, E., Oregi, I., & Moreno-Fernandez-de-Leceta, A. (2021) · Proceedings of the Genetic and Evolutionary Computation Conference Companion · doi:10.1145/3449726.3463123 · Cited by 20 · see citing works
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Mathur, A., Gupta, A., & Das, S. K. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3634143 · Cited by 17 · see citing works
“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 · see citing works
“Other hybrid methods that exploit the complementary strengths of quantum and classical computers can be consulted in Ajagekar et al. (2020) and Ajagekar (2020).”
Background
AutoQUBO 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 · see citing works
“They were shown to provide significant speed-ups for certain combinatorial optimization problems [1, 2].”
Background
Control 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 · see citing works
Quantum Computing Applications in Supply Chain Information and Optimization: Future Scenarios and Opportunities
Shamsuddoha, M., Kashem, M. A., Nasir, T., Hossain, A. I., & Ahmed, M. N. (2025) · Information · doi:10.3390/info16080693 · Cited by 13 · see citing works
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 · see citing works
Quantum-Enhanced Machine Learning
Satuluri, V. K. R. R., & Ponnusamy, V. (2021) · International Conference on Smart Technologies, Communication and Robotics (STCR) · doi:10.1109/stcr51658.2021.9589016 · Cited by 17 · see citing works
Quantum computing for manufacturing and supply chain optimization: enhancing efficiency, reducing costs, and improving product quality
Chen, W. J., Marcus, G. S., & Leesburg, D. (2021) · International Journal of Enterprise Modelling · doi:10.35335/emod.v15i3.48 · Cited by 16 · see citing works
“…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).”
Background
Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing
Yu, S., & Nabil, T. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.730685 · Cited by 13 · see citing works
“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].”
Background
Transit facility allocation: Hybrid quantum-classical optimization
Gabbassov, E. (2022) · PLoS ONE · doi:10.1371/journal.pone.0274632 · Cited by 12 · see citing works
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 12 · see citing works
“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.”
Background
A stochastic data-driven Bayesian optimization approach for intensified ethanol–water separation systems
Ones, O. P., & Flores‐Tlacuahuac, A. (2024) · Chemical Engineering and Processing - Process Intensification · doi:10.1016/j.cep.2024.109708 · Cited by 12 · see citing works
Matheuristics for mixed-model assembly line balancing problem with fuzzy stochastic processing time
Anh, T. T. M., & Hop, N. V. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111694 · Cited by 12 · see citing works
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 · see citing works
“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.”
Background
Adaptive Problem Solving Dynamics in Gate-Model Quantum Computers
Gyöngyösi, L. (2022) · Entropy · doi:10.3390/e24091196 · Cited by 11 · see citing works
NISQ-compatible approximate quantum algorithm for unconstrained and constrained discrete optimization
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
“Hybrid pipelines that combine both quantum and classical approaches are especially beneficial for solving complex optimization problems [10, 11].”
Background
Objective 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 · see citing works
“Objective function examples can be found in 9,24 .).”
Background
Entanglement concentration service for the quantum Internet
Gyöngyösi, L., & Imre, S. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-02716-3 · Cited by 11 · see citing works
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 12 · see citing works
“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…”
Background
Hybrid Quantum Tabu Search for Solving the Vehicle Routing Problem
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“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 · see citing works
“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 10 · see citing works
Integrating quantum computing into building-to-grid control framework: Application of benders decomposition in mixed-integer nonlinear programming
Deng, Z., Wang, X., & Dong, B. (2025) · Building Simulation · doi:10.1007/s12273-025-1248-4 · Cited by 9 · see citing works
“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 citing works
“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.”
Background
Quantum computing as an enabler for sustainable circular economy implementation in Industry 4.0: A study
Jami, A. R., & Haleem, A. (2025) · Human Settlements and Sustainability · doi:10.1016/j.hssust.2025.05.005 · Cited by 11 · see citing works
Supply chain resilience modeling based on dynamic hypergraph and quantum reinforcement learning for low-altitude-ground networks
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Quantum Computing in Transportation Engineering: A Survey
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“…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].”
Influential
Post-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 · see citing works
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 · see citing works
“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 · see citing works

A 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 · see citing works
Self-citation
Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling
Ajagekar, A., Hamoud, K. A., & You, F. (2022) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2022.3187367 · Cited by 50 · see citing works
Self-citation
Quantum Computing for Optimization and Its Applications
Pandian, P., Selvaraj, C., & Yugha, R. (2025) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/979-8-3693-6885-5.ch009
On Quantum Computing for Mixed-Integer ProgrammingPreprint
Chang, C.-Y., Jones, E. B., & Graf, P. A. (2020) · arXiv (Cornell University) · Cited by 6 · see citing works
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 · see citing works

Authors: Ajagekar, A., Humble, T., & You, F.
Affiliations: Cornell University (US)
Oak Ridge National Laboratory (US)
Corresponding author: You, Fengqi
ORCID: Ajagekar, Akshay
Humble, Travis
You, Fengqi
Venue: Computers & Chemical Engineering
Publisher: Elsevier BV
Published: 2020-01
Volume: 132
Pages: 106630
ISSN: 0098-1354 (ISSN-L)
DOI: 10.1016/j.compchemeng.2019.106630
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 310 d)
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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