Quantum computing for energy systems optimization: Challenges and opportunities
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Ajagekar and You applied QA to three energy systems problems: facility location allocation, unit commitment, and heat exchanger networks.
“Ajagekar and You [3] apply QA to three problems from energy systems, namely, facility location allocation, unit commitment, and heat exchanger networks”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [3]
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“Various quantum optimization algorithms, including QAOA, Quadratic Unconstrained Binary Optimization (QUBO), and Quantum Annealing Algorithm (QAA), are employed for solving the UC problem [173], [174], [175], [176].”
BackgroundMethodAdapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment
Koretsky, S., Gokhale, P., Baker, J. M., Viszlai, J., Zheng, H., Gurung, N., Burg, R., Paaso, E. A., Khodaei, A., Eskandarpour, R., et al. (2021) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce52317.2021.00035 · Cited by 45 · see citing works
“It aims to minimize operational cost while meeting a target power load using a number of power-generating units that are subject to constraints [2].”
BackgroundInfluentialPerspectives of quantum computing for chemical engineering
Bernal, D. E., Ajagekar, A., Harwood, S. M., Stober, S. T., Trenev, D., & You, F. (2022) · AIChE Journal · doi:10.1002/aic.17651 · Cited by 46 · see citing works
Self-citationJia, W., Ding, T., & He, Y. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116375 · Cited by 54 · see citing works
Advanced Modeling and Optimization Strategies for Process Synthesis
Pistikopoulos, E. N., & Tian, Y. (2024) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-100522-112139 · Cited by 40 · see citing works
Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community
Mastroianni, C., Plastina, F., Scarcello, L., Settino, J., & Vinci, A. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3286106 · Cited by 40 · see citing works
“In the last few years, research and industrial efforts are showing that quantum computing can offer the opportunity to approach energy optimization problems with a completely different paradigm [8], [9].”
BackgroundAjagekar, A., & You, F. (2024) · Advances in Applied Energy · doi:10.1016/j.adapen.2024.100179 · Cited by 41 · see citing works
Self-citationNew frontiers of quantum computing in chemical engineering
Ajagekar, A., & You, F. (2022) · Korean Journal of Chemical Engineering · doi:10.1007/s11814-021-1027-6 · Cited by 38 · see citing works
Self-citationQuantum Computing in Supply Chain Management State of the Art and Research Directions
Gachnang, P., Ehrenthal, J. C. F., Hanne, T., & Dornberger, R. (2022) · Asian Journal of Logistics Management · doi:10.14710/ajlm.2022.14325 · Cited by 47 · see citing works
Satpathy, S. K., Vibhu, V., Behera, B. K., Al-Kuwari, S. M., Mumtaz, S., & Farouk, A. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3300577 · Cited by 35 · see citing works
“Unfortunately, large processing power is required to accomplish such optimization on a large scale [19], including power generation, transmission, and distribution [20].”
BackgroundAjagekar, A., & You, F. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123244 · Cited by 34 · see citing works
Self-citationQuantum computing: Impact on energy efficiency and sustainability
Sood, V., & Chauhan, R. P. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124401 · Cited by 36 · see citing works
Quantum computing basics, applications and future perspectives
Balamurugan, K. S., Sivakami, A., Mathankumar, M., Prasad, Y. J. D. S., & Ahmad, I. (2024) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2024.137917 · Cited by 32 · see citing works
Quantum-Soft QUBO Suppression for Accurate Object Detection
Li, J., & Ghosh, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_10 · Cited by 31 · see citing works
“ilarity between each pair of the proposed regions. 3.2 Quantum Annealing Quantum Annealing has been shown to be advantageous over classical algorithms in terms of computational time for QUBO problems [1]. Classical algorithm such as Tabu Search [23] reduces time complexity from exponential to polynomial at the expense…”
BackgroundInfluentialQuantum technologies for climate change: Preliminary assessmentPreprint
Berger, C., Paolo, A. D., Forrest, T., Hadfield, S., Sawaya, N. P. D., Stęchły, M., & Thibault, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05362 · Cited by 47 · see citing works
“, wind turbine placement [58], hybrid energy system design [59, 60], and in automotive industry applications [61].”
BackgroundSolving a real-world package delivery routing problem using quantum annealers
Osaba, E., Villar-Rodríguez, E., & Asla, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-75572-1 · Cited by 29 · see citing works
Deep learning-enhanced quantum optimization for integrated job scheduling in container terminals
Cuong, T. N., Kim, H., Long, L. N. B., You, S., & Tan, N. D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110431 · Cited by 28 · see citing works
Rawat, K. S., & Yadav, M. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10312-w · Cited by 29 · see citing works
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
Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning
Maior, C. B. S., Araújo, L. M. M., Lins, I. D., Moura, M. D. C., & Droguett, E. L. (2023) · IEEE Access · doi:10.1109/access.2023.3255417 · Cited by 27 · see citing works
“Although the subject is promising and has increased its recognition, the research is still limited, among other factors, because of the size of the problems, given the amount of quantum bits (qubits) available in simulators and computers [30].”
BackgroundCelsi, M. R., & Celsi, L. R. (2024) · Energies · doi:10.3390/en17051039 · Cited by 27 · see citing works
“…first two constraints represent energy balance, and the third one is a logical constraint. such that ∑ j q ij = s i , ∀ i = 1, 2, . . . , m , This problem was actually solved on D-Wave’s 2000Q quantum processor [47], with considerable benefit with respect to a solution based on classical computers.”
BackgroundAn investigation of IBM quantum computing device performance on combinatorial optimisation problems
Khumalo, M. T., Chieza, H. A., Prag, K., & Woolway, M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07438-4 · Cited by 27 · see citing works
“Specifically, through the use of Noisy Intermediate-Scale Quantum (NISQ) technology, preliminary findings for both the TSP [15, 16] and QAP [7] have been reported.”
InfluentialQuantum computing for power systems: Tutorial, review, challenges, and prospects
Liu, H., & Tang, W. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2023.109530 · Cited by 29 · 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-Computing-Inspired Optimal Power Allocation Mechanism in Edge Computing Environment
Bhatia, M., & Sood, S. K. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3358900 · Cited by 26 · see citing works
“Ajagekar and You [11] introduced a QCT methodology to reduce wait times in cloud computing.”
MethodNext Generation Power System Planning and Operation With Quantum Computation
Ganeshamurthy, P. A., Ghosh, K., O’Meara, C., Cortiana, G., Schiefelbein, J., & Monti, A. (2024) · IEEE Access · doi:10.1109/access.2024.3509743 · Cited by 30 · see citing works
Building Knowledge Capacity for Quantum Computing in Engineering Education
Carberry, D. E., Nourbakhshrezaei, A., Karon, J., Jones, M., Jadidi, M., Shahriari, K., Beenfeldt, C., Andersson, M., & Mansouri, S. S. (2021) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-323-88506-5.50319-3 · Cited by 25 · see citing works
Hong, Y., Lopez, D. J. D., & Wang, Y.-Y. (2024) · IEEE Access · doi:10.1109/access.2024.3472053 · Cited by 23 · see citing works
“Quantum computing is an emerging technology that leverages concepts such as quantum bits or ‘‘qubits,’’ superposition, and entanglement to represent and process information [11], [12], [13].”
BackgroundQuantum computing in renewable energy exploration: status, opportunities, and challenges
Olatunji, O. O., Adedeji, P. A., & Madushele, N. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-824555-2.00019-8 · Cited by 23 · see citing works
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
Safari, A., & Badamchizadeh, M. A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10201-6 · Cited by 22 · see citing works
Sikiru, I. A., Kora, A. D., Ezin, E. C., Imoize, A. L., & Li, C. (2024) · Electronics · doi:10.3390/electronics13214153 · Cited by 23 · see citing works
“These novel research opportunities have been well amplified by the research community in the niche of optimization with applications in operational planning [94], molecular design [95], process scheduling and operations [33], logistics optimization [96], and energy systems [95].”
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.”
BackgroundQuantum Machine Learning: Unraveling a New Paradigm in Computational IntelligencePreprint
Khurana, S., & Nene, M. J. (2024) · doi:10.22541/au.171037552.27623320/v1 · Cited by 22 · see citing works
Santos, V. O., Marinho, F. P., Rocha, P. A. C., Thé, J. V. G., & Gharabaghi, B. (2024) · Energies · doi:10.3390/en17143580 · Cited by 21 · see citing works
“The superiority of the classical models over the QNN approach is due to their much more mature implementation in a classical device, whilst QML software remains in its early stages [106], employing a very limited number of qubits.”
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
“Ajagekar and You [3] apply QA to three problems from energy systems, namely, facility location allocation, unit commitment, and heat exchanger networks, whereas Ding et al. [39] used QA for the prediction of financial crashes [39].”
MethodUnsupervised 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
Experimental Study of the Optical Qubit on the 435-nm Quadrupole Transition in the 171Yb+ Ion
Zalivako, I. V., Semerikov, I. A., Borisenko, A. S., Aksenov, M. A., Khabarova, K. Y., & Kolachevsky, N. (2021) · Journal of Experimental and Theoretical Physics Letters · doi:10.1134/s0021364021140113 · Cited by 19 · see citing works
Advances in Virtual Power Plant Operations: A Review of Optimization Models
Marzbani, F., Osman, A. H., & Hassan, M. S. (2025) · IEEE Access · doi:10.1109/access.2025.3591697 · Cited by 19 · see citing works
“…its direct application to VPP scheduling and management is still at an early stage, recent studies and proof-of-concept experiments demonstrate that quantum algorithms can effectively address combinatorial and stochastic challenges that often limit the performance of classical methods [125], [126].”
Hybrid Quantum-Classical Unit Commitment
Mahroo, R., & Kargarian, A. (2022) · IEEE Texas Power and Energy Conference (TPEC) · doi:10.1109/tpec54980.2022.9750763 · Cited by 19 · see citing works
“In this regard, to solve the UC problem, the authors in [10] have proposed two modifications to enable using quantum algorithms:”
MethodQuantum 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)).”
MethodAl-Shafei, A., Zareipour, H., & Cao, Y. (2022) · Energies · doi:10.3390/en15228668 · Cited by 18 · see citing works
Gyöngyösi, L., & Imre, S. (2025) · Quantum Information and Computation · doi:10.2478/qic-2025-0006 · Cited by 18 · see citing works
Quantum Hardware Devices (QHDs): Opportunities and Challenges
Oukaira, A. (2025) · IEEE Access · doi:10.1109/access.2025.3576216 · Cited by 19 · see citing works
“[72], [73], [74], [75].”
Fermionic quantum approximate optimization algorithm
Yoshioka, T., Sasada, K., Nakano, Y., & Fujii, K. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.023071 · Cited by 17 · see citing works
“The targets are combinatorial optimization problems in industry, such as financial optimization [1, 2], logistics and distribution optimization [3, 4], and energy optimization [5].”
BackgroundOlatunji, K. O., Madyira, D. M., & Adeleke, O. (2023) · Fuel · doi:10.1016/j.fuel.2023.129334 · Cited by 17 · see citing works
Application of Quantum Computing in Reliability Analysis
Yazdi, M. (2024) · Springer series in reliability engineering · doi:10.1007/978-3-031-53514-7_8 · Cited by 17 · see citing works
Yang, H., Liu, Y., Yue, Y., Zhang, D., & Ma, Y. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.110957 · Cited by 17 · see citing works
van Erp, T., & Gładysz, B. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.05.118 · Cited by 16 · see citing works
Wulff, A., Chen, B., Steinberg, M., Tang, Y., Möller, M., & Feld, S. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117380 · Cited by 16 · see citing works
“As this technology continues to mature, the potential benefits it offers to complex engineering problems are becoming more apparent [10–23].”
BackgroundEntanglement 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
“Ajagekar and You [17] explored quantum computing applications in energy systems optimization and addressed associated challenges.”
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
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
Dave, K., Innan, N., Behera, B. K., Mumtaz, S., Al-Kuwari, S. M., & Farouk, A. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3540949 · Cited by 14 · see citing works
“However, quantum computing (QC), particularly quantum machine learning (QML) algorithms, have the potential to overcome these limitations by providing new techniques to reduce carbon footprints and increase energy efficiency in IIoT systems [14], [15].”
Mandal, A. K., Nadim, M., Roy, C. K., Roy, B., & Schneider, K. A. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00493-w · Cited by 16 · see citing works
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
Kundu, S., Gupta, T., Sardar, A., Bandyopadhyay, A., Swain, S., & Mallik, S. (2025) · Franklin Open · doi:10.1016/j.fraope.2025.100371 · Cited by 19 · 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
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
“Quantum computing has the potential to address the complexities of these global operations by optimizing large-scale decision-making processes (Ajagekar & You, 2019)(Ajagekar, 2020).”
BackgroundMethodInfluentialQOPTLib: A Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems
Osaba, E., & Villar-Rodríguez, E. (2023) · Springer tracts in nature-inspired computing · doi:10.1007/978-981-99-3970-1_4 · Cited by 13 · see citing works
A Quantum Computing Approach for the Unit Commitment Problem
Halffmann, P., Holzer, P., Plociennik, K., & Trebing, M. E. (2023) · Lecture notes in operations research · doi:10.1007/978-3-031-24907-5_14 · Cited by 13 · see citing works
“So far, only two contributions try to solve the UCP via quantum computing: In [1], a UCP problem with quadratic cost function, demand satisfaction and minimum and maximum power generation is discussed.”
BackgroundAdvancements in Quantum Optics: Harnessing the Power of Photons for Next-Generation Technologies
Shaker, L. M., Al‐Amiery, A. A., Isahak, W. N. R. W., & Al‐Azzawi, W. K. (2023) · Journal of Optics · doi:10.1007/s12596-023-01320-9 · Cited by 13 · see citing works
Xu, Y., He, Y., Yang, J., Zhou, Y., Wu, A., Yu, C., Zhai, Y., Liu, Y., Wu, D., Guo, H., et al. (2024) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2024.125369 · Cited by 13 · see citing works
Operation of distribution network: Challenges and opportunities in the era of peer-to-peer trading
Razavi, S.-M., Haghifam, M., Arefizadeh, S., Larimi, S. M. M., & Shafie‐khah, M. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.04.053 · Cited by 13 · see citing works
Transforming future technology with quantum-based IoT
Khan, H. U., Nasir, A., Ali, F., & Nazir, S. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06251-1 · Cited by 13 · see citing works
“T h i s i n c l ud e s a dd r e ss i ng e qu i p m e n t fl a w s a nd e nh a n c i ng d e p e nd a b ilit y [ 71 ] 1 3 Transforming future technology with quantum-based IoT concentrating their efforts on developing more efficient and safe algorithms [74].”
InfluentialYu, S., & Nabil, T. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.730685 · Cited by 13 · see citing works
“…experiments carried out on D-Wave ’ s 2000-qubit machine have shown that quantum annealing, when combined with classical solvers within an hybrid approach, can outperform some existing commercial solvers on industrial scale optimization problems, such as scheduling and traf fi c control [29, 30].”
MethodArchitecture decisions in quantum software systems: An empirical study on Stack Exchange and GitHub
Aktar, M. S., Liang, P., Waseem, M., Tahir, A., Ahmad, A., Zhang, B., & Li, Z. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107587 · Cited by 12 · see citing works
- Authors
- Ajagekar, A., & You, F.
- Affiliations
- Cornell University (US)
- Corresponding author
- You, Fengqi
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 179
- Pages
- 76-89
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2942962312
- Cited by
- 289
- References
- 76
- Citations by year
- 2026: 40 · 2025: 68 · 2024: 64 · 2023: 37 · 2022: 34 · 2021: 26 · 2020: 16 · 2019: 4
- FWCI
- 9.23
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Energy (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- Cornell University's
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- quantum computing, energy system optimization, quantum algorithms, facility location, unit commitment, heat exchanger network synthesis, quantum computing hardware, power systems
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