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Quantum computing for energy systems optimization: Challenges and opportunities
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“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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BackgroundQuantum 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 the quantum algorithm proposed by Shor for factorization which is exponentially faster than any known classical algorithm [12], and Grover’s quantum search algorithm capable of searching a large database in time square root of its size [13].”
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
Dowsland, K. A., & Thompson, J. M. (2012) · Springer eBooks · doi:10.1007/978-3-540-92910-9_49 · Cited by 2,470
Quantum annealing in the transverse Ising model
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Cited by
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Items 201–289 of 289
Comparative study of quantum and classical algorithms for renewable energy sources
Sinha, P. K., & Marimuthu, R. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107062 · Cited by 2
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
“optimization [5], operational planning [6], and energy systems [7].”
BackgroundSelf-citationA Quantum Probabilistic Comparator: Circuit Implementation
Nguyen, L., & Jiang, Z. (2023) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon58068.2023.10365860 · Cited by 2
Mehta, K., & Sharma, R. (2024) · Wiley eBooks · doi:10.1002/9781394214334.ch2 · Cited by 2
Hangun, B., Eyecioglu, O., Ali, M., Altun, O., & Kayisli, K. (2025) · Research Square · doi:10.21203/rs.3.rs-9475883/v1 · Cited by 2
Quantum-Inspired Algorithms for Market Clearing in Smart Grids: A Comprehensive Review
Jha, R. K. (2023) · Recent Research Reviews Journal · doi:10.36548/rrrj.2023.2.007 · Cited by 1
Exploring the future of urban death: Speculative design and the concept of necropolis 4.0
Rahimi, F. B. (2025) · Cities · doi:10.1016/j.cities.2025.105859 · Cited by 1
Classification Using Hybrid Classical-Quantum Machine Learning
Bala, R., Kher, G., Singh, V., & Singh, R. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-84059-3_14 · Cited by 1
Yu, X. W., Xie, H., & Fu, W. (2025) · iEnergy · doi:10.23919/ien.2025.0009 · Cited by 1
Weng, Z., Xu, Z., Li, R., Wang, L., Li, Z., Zhang, Y., & Li, M. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128142 · Cited by 1
Quantum Annealing in Power System Optimisation: A Review
Ngo, V. T. A., Zhang, Y., Nguyen, K., Yang, S. Y. S., & Mishra, Y. (2025) · Australasian Universities Power Engineering Conference · doi:10.1109/aupec66173.2025.11219460 · Cited by 1
Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment
Peter, B., & Korkali, M. (2025) · North American Power Symposium (NAPS) · doi:10.1109/naps66256.2025.11272398 · Cited by 1
“Tangential problems in power grid research are highlighted in [7], which discusses the use of quantum algorithms to solve power flow problems, and in [8], which explores the use of quantum computing to optimize the integration of renewables in decarbonizing energy systems.”
Quantum Opacity, Classical Clarity: A Hybrid Approach to Quantum Circuit Obfuscation
Raj, A., & Balachandran, V. (2025) · doi:10.1145/3733817.3762698 · Cited by 1
“…and entanglement, quantum bits—or qubits—can exist in multiple states simultaneously, allowing quantum computers to tackle problems that are computationally hard for classical systems, such as factoring large numbers [2], simulating molecular interactions [3], or optimizing complex systems [4].”
Application of Innovative Quantum Computing Technologies for Precision Agriculture—A Study
Thankappan, J. C., & Kumar, M. B. S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9191-3_48 · Cited by 1
Machine learning for fuel cell remaining useful life prediction: A review
Allal, Z., Noura, H., Vernier, F., Salman, O., & Chahine, K. (2026) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2026.101597 · Cited by 1
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
“Research into QC’s capabilities is expanding into diverse fields, including transportation (Cooper 2022; Dixit and Jian 2022; Dixit and Niu 2023; Dixit, Niu, Rey, et al. 2023), engineering design (Chen et al. 2022), finance (Bova, Goldfarb, and Melko 2021), and energy system (Ajagekar and You 2019).”
Quantum Computing Applications in Engineering: Current Status and Future Prospects
Rukmanikanthan, S., Dullah, H., Teoh, Y. X., & Ahmed, A. N. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10569-9 · Cited by 1
“…(A) [ 52, 63 ], structural eigen-mode analysis aligns with class (B) [53 ], and power grid restoration/dispatch commonly combine class (A) encodings with hybrid workflows [ 14, 63 , 64 ], while traffic flow optimisation reflects class (A) with strong real-time and scaling constraints [ 54 –56 ].”
Fukushima, K., Ohno, H., & Fukushima, Y. (2026) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2026.06.032 · Cited by 1
Quantum Optimization Techniques and It’s Comparison with Classical Optimization
Kadam, P. P., & Kadam, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-0769-4_55 · Cited by 1
A Comprehensive Review of Metaheuristic Algorithms Inspired by Quantum Mechanics
Biswal, S. S., Swain, D. R., Rout, P. K., Das, A., & Mishra, A. (2023) · International Conference in Advances in Power, Signal, and Information Technology (APSIT) · doi:10.1109/apsit58554.2023.10201716 · Cited by 1
“Because of its fundamental concepts, quantum computing introduces novel concepts to optimization, as well as being able to process a large number of quantum states concurrently parallel [15].”
BackgroundLearning Quantum System Disturbance Models with Probabilistic Bayesian Neural Networks
Jiang, Z., & Nguyen, L. (2023) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon58068.2023.10365822 · Cited by 1
Quantum Slime Mould Algorithm and Application to Urgent Transportation
Khelfa, C., Drias, H., & Khennak, I. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-59318-5_7 · Cited by 1
Advancing Network Security with Quantum-Safe System Integration
Mao, J., Xu, G., Sakk, E., & PaulWang, S. (2024) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn61486.2024.10637630 · Cited by 1
“This unique property gives quantum computers the potential to outperform classical computers, enabling them to achieve exponential speedups on specific problems such as prime factorization of large numbers [3], optimization tasks [4], and artificial intelligence applications [5].”
BackgroundChatterjee, A., Das, S. K., & Ghosh, S. (2025) · Sensors · doi:10.3390/s25061854 · Cited by 1
“…mechanics principles to perform tasks unachievable by classical computing, with applications spanning molecule simulation for drug development, financial modeling improvements, machine learning advancements, optimization task enhancements, and supply chain management transformations [1]–[5].”
Full-Stack Quantum Computing and Distributed Systems: A Community-Centric Approach
Feld, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94263-1_1 · Cited by 2
Ahmed, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-7160-2.ch013 · 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
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
Fairness evaluation during the conceptual design of heat grids with quantum annealersPreprint
Loh, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.09929 · Cited by 1
“As we are trying to solve a graph partition problem in the energy domain, we are only aware of the work [1] in energy systems optimization using quantum computing systems.”
MethodQuantum Natural Gradient for Variational BayesPreprint
Lopatnikova, A., & Tran, M. (2021) · arXiv (Cornell University) · Cited by 1
Q-Pandora Unboxed: Characterizing Noise Resilience of Quantum Error Correction CodesPreprint
Chatterjee, A., Das, S. K., & Ghosh, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3663236/v1 · Cited by 1
A Comprehensive Study of Quantum Computing Technologies in Smart City: Review and Future DirectionsPreprint
Chen, H., Azzoui, A. E., Park, H., Camacho, D., & Park, J. H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4661705 · Cited by 2
Lv, B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5252103 · Cited by 1
Planning and Design Method of Integrated Energy Station in Complex Terrain
Yang, J., Sun, B. N., Zhang, L., & Hou, Y. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9728580
“With the upgrade of the design scale, the calculation difficulty of the IES site selection planning has increased exponentially [18].”
MethodModern Approaches to Topological Quantum Error Correction
Parrado-Rodríguez, P. (2022) · Cronfa (Swansea University) · doi:10.23889/suthesis.60302
Khumalo, M. T., Prag, K., & Nixon, K. J. (2022) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi56532.2022.10068445 · Cited by 1
“Relative cost challenges are predominant to direct cost challenges in industry scenarios [7], [8].”
BackgroundMethodQuantum Optimization for Redundancy Allocation Problem Considering Various Subsystems
Lins, I. D., Araújo, L. M. M., Maior, C. B. S., Teixeira, E. S., Bezerra, P. T. L., Moura, M. J. D. C., & Droguett, E. L. (2023) · European Safety and Reliability Conference (ESREL) · doi:10.3850/978-981-18-8071-1_p225-cd
“To effectively handle combinatorial optimization problems such as RAP, several Ising model-based computers, also known as annealing machines, have been developed (Ajagekar et al., 2019).”
MethodTechnology Landscape for Making Climate and Environmental Science “Actionable”
Sun, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-41758-0_3
Alvarez‐Alvarado, M. S., Apolo-Tinoco, C., Ramirez-Prado, M. J., Alban-Chacón, F. E., Pico, N., Aviles-Cedeno, J., Recalde, A. A., Moncayo-Rea, F., Velásquez, W., & Rengifo, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4687769
Technologies in Sustainable Supply Chain: Insights from a Systematic Literature Review
Siju, N. M., & Shivdas, A. (2024) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1479-351220240000036027
On the benefits of a new continuous reformulation for QUBO problems
Bartmeyer, P. M., & Lyra, C. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-05887-9
“Thus,adiabaticquantumcomputingusingQUBOformulationsmay provideawayforwardforreal-worldapplicationsinenergysystems(Ajagekar&You,2019; Morstyn,2023;Golestanetal.,2023),machinelearning(Dateetal.,2021),andfinance(Orús et al., 2019).”
Khezri, A., Wärmefjord, K., & Dantan, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4876099
Quantum-based PRNG for image encryption: a comprehensive study
Cruz, L. J. M. S., Faina, L. F., & Pereira, J. H. D. S. (2024) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2024.2408614
“Some of these new methods, techniques, and algorithms are being used to solve real-world optimization problems (Ajagekar and You 2019; Sigov, Ratkin, and Ivanov 2022; Krenn, Erhard, and Zeilinger 2020).”
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
Yoshioka, T., Sasada, K., Nakano, Y., & Fujii, K. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00061
“Quantum computation is expected to have the potential to efficiently solve combinatorial optimization problems in such power systems [11]–[13].”
Optimization Method for Microgrid Demand Response Based on Coherent Photonic Quantum Computing
Wan, Y., Tian, G., Xi, H., & Fang, Y. (2024) · International Conference on Power and Energy Systems (ICPES) · doi:10.1109/icpes63746.2024.10856580
Quantum Computing for Business Applications
Shankar, S. P., Varadam, D., Nida, S., Trisha, U., -, S. C., Shree, S. C., Ankitha, R., & Bharadwaj, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6225-9.ch008
Xu, X., Pang, H., Liu, Q., & Liu, M. (2024) · International Conference on Information Education and Artificial Intelligence (ICIEAI) · doi:10.1145/3724504.3724577
“Its theoretical foundation originates from quantum physics [1], a field that studies matter-energy interactions at the microscopic scale [11].”
From Theory to Reality: Tracing the Milestones of Quantum Information Systems
Miah, M. S. U., Islam, S. S., & Bhowmik, A. (2024) · International Conference on Computing Advancements (ICCA) · doi:10.1145/3723178.3723269 · Cited by 1
“In the energy sector, QIS can optimize power grid management and improve material science research for better energy storage solutions [5].”
The Art of Quantum Computing for Finance: Brief Overview and ProspectsPreprint
Mengara, O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5373357
The Art of Quantum Computing for Finance: Brief Overview and ProspectsPreprint
Mengara, O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5362526
Sankaradass, V., Tholkapiyan, M., Sudhakar, S., & Devasenan, R. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04894-4
“Quantum computing has already been implemented as an innovative solution in case of such problems in computational data and optimisation [6, 7].”
Abdolahi, R., Soleymani, M. R., & Hamouda, W. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161958
“Recently, quantum computing has provided new tools for addressing combinatorial optimization challenges like [10, 11].”
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
“Annealing-based hybrid approaches are utilized for mixed-integer linear pro-grammingproblems[16–18],includingUCproblems[19,20],demonstratingpotential quantumadvantages.”
Multi-Objective Quantum Power System Redispatch
Lee, L. K., Gerlach, T., Knaute, J., Gerhardt, F., Völker, P., Maras, T., Dotterweich, A., & Piatkowski, N. (2025) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa65442.2025.11247977
“In recent years, there has been a growing interest in addressing challenges related to economic dispatch and power system optimization [10].”
Noise-Aware Quantum Annealing for State Estimation in Power Systems
Tran, Q.-B., & Dinh, T. N. (2025) · International Conference on Quantum Computing and Engineering · doi:10.1109/qce65121.2025.00212 · Cited by 2
“We evaluate our approach on two standard power network topologies: a 4-bus system adapted from [11] and the IEEE 5-bus test case.”
A Hybrid QUBO-Reinforcement Learning Framework for Intelligent Traffic Congestion Optimization
Hemkiran, S., Shree, A., V, R, A. L., & Manashwini, C. (2025) · International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM) · doi:10.1109/itechsecom64750.2025.11307241
“In addition to isolated QUBO and RL methods, hybrid quantum-classical logistics and optimization literature revealed increasing interest in hybridization [7,8].”
AI in Smart Grids for Enhanced Renewable Energy Management: Part 2. Challenges and Future Directions
Nedaei, A., Eskandari, A., Salehpour, S., Ghaedi, P., Shafie‐khah, M., & Aghaei, M. (2025) · International Conference on Future Energy Solutions (FES) · doi:10.1109/fes65767.2025.11341788
“This computational burden increases costs and slows the development cycle for utilities seeking to deploy advanced analytics, potentially putting these technologies out of reach for smaller operators with limited IT infrastructure [10].”
Enablers and Barriers in Climate and Green Finance: A Hybrid ReviewPreprint
Akhtar, F., Akhtar, S. M., & Laeeq, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6135522
Grover Algorithm-based Quantum Scheduling Framework for Constrained Graph Coloring
Sausan, L., & Lee, H. (2025) · Journal of Korean Institute of Industrial Engineers · doi:10.7232/jkiie.2025.51.6.454
Janani, N., Kumar, T. V., Shree, K. D., Kumar, A. V. S., & Karthiga, M. (2026) · Springer eBooks · doi:10.1007/978-3-032-15188-9_9
Khalil, H., Elshazly, O., & Shaheen, O. (2026) · Scientific Reports · doi:10.1038/s41598-026-37973-2 · Cited by 1
Farina, M., Ding, X., Yu, X. X., & Lavazza, A. (2026) · NanoEthics · doi:10.1007/s11569-026-00502-6
Real-Time Supply Chain Visibility via Integrated IoT and AI
Farag, W. (2026) · International Conference on Knowledge and Smart Technology · doi:10.1109/kst67832.2026.11431773
“Ajagekar and You [27] demonstrated that quantum approaches could solve certain SC optimization problems 100-1000x faster than classical methods for large-scale instances.”
Networking and protocols for AI renewable energy networks
Ahmed, M., Ahmed, M., & Qureshi, K. N. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40618-8.00001-2
Resilience-Constrained Low-Carbon Dispatch of Industrial Parks with Storage and Quantum Acceleration
Li, W., Li, C., Zhang, X., Xu, S., Yue, Y., & Zhang, H. (2026) · Processes · doi:10.3390/pr14061024
Ramachandiran, R., Kawade, B. P., Nidhya, M. S., Kawoosa, A. I., Sanamdikar, S. T., & Karthikeyan, M. P. (2026) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl67539.2026.11451977
“The quantum annealing algorithm and variational quantum algorithms have proved to be more beneficial in solving combinatorial problems, including scheduling, routing, and resource allocation [4].”
Quantum cryptanalysis of SPN ciphers with known plaintext
Kiran, U., Safdar, A., Khalid, S. U., Butt, H. T., & Jalil, Z. (2026) · npj Quantum Information · doi:10.1038/s41534-026-01218-x
A Two-Step Quantum Approximate Optimization Algorithm for Portfolio Optimization and Risk Assessment
Wu, B.-X., & Wang, L. (2026) · Quantum Reports · doi:10.3390/quantum8020045
“…span a wide range of industries [7,8], such as logistics, where it can optimize transportation and distribution [9]; healthcare, for accelerating drug discovery and improving gene sequencing [10,11]; and energy, for optimizing the consumption patterns and enhancing utilization efficiency [12].”
Quantum assisted genetic algorithm for multi-objective optimization
Kansara, R., Serrano, M. I. R., & Bähr, M. (2026) · Physica Scripta · doi:10.1088/1402-4896/ae6bcd
Avcı, A. B. (2026) · Indoor Air · doi:10.1155/ina/3385686
Bruckner, S., Hillmich, S., Ramler, R., & Ferrarotti, F. A. (2026) · ACM Symposium on Applied Computing · doi:10.1145/3748522.3779897
“Other fields, where quantum computing is likely to find practical application, are the finance sector, where portfolio optimization [10] is a prominent example, as well as the energy sector [1].”
Zare, A., Longo, M., & Zaninelli, D. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-29254-4_32
Pandey, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0156-1.ch006
Deep learning and quantum-enhanced predictive optimization in power electronics
Jahan, M., Mustavy, M. R. A., & Bhuyan, P. D. E. M. H. (2026) · Smart Cities and Advanced Technology · doi:10.21595/scat.2026.25893
Quantum fourier transform for unsupervised fault detection in building end-use electricity loads
Zia, H., Malkawi, A. M., & Han, J. M. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117900
Hong, Y., & Lopez, D. J. D. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3697311
“As shown in Table I, prior studies [9], [10], [11], [12], [13], [14], [15], [16], [17], [18] are limited to small systems (fewer than ten generators) and short horizons (6–12 h) and uniformly omit spinning reserve constraints.”
Digital Twin Maturity Level Effects on Microgrid Resilience Validated Through Monte Carlo Simulation
MORARU, A.-A. (2026) · Proceedings of the ... International Conference on Business Excellence · doi:10.2478/picbe-2026-0149
“The integration of DERs like solar, wind, and electric fleets introduces a complexity of power networks that is expected to only keep increasing (Ajagekar & You, 2019).”
Quantum Technologies as a Driver of Future Industrial Policy
Narin, K. T., & Öz, S. (2026) · Industrial Policy · doi:10.61192/indpol.1829442
Quantum Annealing Applications for Power Grid Modernization
Christeson, T. (2025) · Digital Commons - DU (University of Denver) · doi:10.56902/etdcrp.2025.223
Tightiz, L., Dang, L. M., & Yang, H.-S. (2026) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2026.105312
Guo, Y., Hu, Y., Wang, K., Wang, D., & Chu, C. (2026) · Frontiers in Energy Research · doi:10.3389/fenrg.2026.1886350
GPU-Based Hybrid Quantum Computing for EV Charging Station Decision Planning–A Case Study
Lopez, D. J. D., Hong, Y.-Y., Purio, M. A., Apolinario, G. F. D., Wang, Y.-Y., Huang, E., & See, S. (2026) · IEEE International Conference on Quantum Control, Computing and Learning (qCCL) · doi:10.1109/qccl70331.2026.11668572
“Prescriptive methods such as optimization may also benefit, as quantum effects like tunneling can help escape local minima in complex objective landscapes [2].”
Yoshioka, T., Sasada, K., Usuki, R., Nakano, Y., & Fujii, K. (2026) · Physical Review Applied · doi:10.1103/568y-4264 · Cited by 2
Subagyo, T., Nugroho, W. S., Purnami, P., Sofi’i, Y. K., Wahid, A. M. S., Mahardika, M. B., & Wardana, I. N. G. W. I. N. G. (2026) · Next Energy · doi:10.1016/j.nxener.2026.101049
Hangun, B., Altun, O., & Eyecioğlu, Ö. (2026) · SN Computer Science · doi:10.1007/s42979-026-05384-9
“QML applications in the energy domain include quantum kernel methods for solar irradiance prediction [13], quantum ML for transient stability assessment [ 14], quantum optimization for energy system design [15], and quantum key distribution for grid communication security [16, 17].”
Maletin, N. V., Горшков, К. Е., Safonov, V. I., Minaev, D. V., Павлович, К. С., Moiseevsky, A. D., Коржов, А. В., Лонзингер, П. В., Surin, V. A., Gorshkov, E. E., et al. (2026) · Laser Physics Letters · doi:10.1088/1612-202x/aeab58
“Examples of using quantum computers for calculating steady-state parameters of small-scale ( ∼ 10 nodes) model EPSs have appeared in the scientific literature in recent years (see, e.g. [13–17]).”
Metadata
- 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 › Physics and Astronomy › Atomic and Molecular Physics, and Optics › 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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