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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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Cites
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“In the field of chemistry and optimization, a classical-quantum hybrid algorithm called Variational Quantum Eigensolver (VQE) has shown exceptional performance in terms of speed and resource utilization [31].”
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
Sorted by citations
Items 101–200 of 289
Hybrid quantum annealing decomposition framework for unit commitment
Ling, J., Zhang, Q., Geng, G., & Jiang, Q. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.111121 · Cited by 14
A Lagrangean decomposition optimization approach for long-term planning, scheduling and control
Mora‐Mariano, D., Gutiérrez‐Limón, M. A., & Flores‐Tlacuahuac, A. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.106713 · Cited by 12
Le, T. T., Huynh, L. T. V., Nguyen, H. N. N., & Doan, Q. N. (2025) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.70147 · Cited by 11
Adaptive Problem Solving Dynamics in Gate-Model Quantum Computers
Gyöngyösi, L. (2022) · Entropy · doi:10.3390/e24091196 · Cited by 12
Nguyen, P.-H., Pham, T.-V., Nguyen, L.-A. T., Narayanamoorthy, S., Nguyen, T.-H. T., & Vu, T.-G. (2024) · IEEE Access · doi:10.1109/access.2024.3439629 · Cited by 13
“Furthermore, as highlighted by Ajagekar and You [59], Energy Consumption (BAR17) poses another significant challenge.”
BackgroundHuo, D., Gu, W., Guo, D., & Tang, A. (2024) · Energy Economics · doi:10.1016/j.eneco.2024.107976 · Cited by 11
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
“Objective function examples can be found in 9,24 .).”
BackgroundYang, X., Wu, D., & Zheng, Z. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/8523480 · Cited by 14
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
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
“…may be able to perform certain computational tasks exponentially quicker than conventional computers(Marella & Parisa, 2020)(Gyongyosi & Imre, 2019)(Cacciapuoti et al., 2019)(Preskill, 2012)(Jha, 2021)(Cusumano, 2018)(Ajagekar & You, 2019)(Wittek, 2014)(Jhanwar & Nene, 2021)(Deodoro et al., 2021).”
BackgroundArshad, M. W., Lodi, S., & Liu, D. Q. (2025) · Machines · doi:10.3390/machines13030204 · Cited by 10
Optimal PMU Placement via Quantum Optimization
Jiang, Y., Liang, Z., Li, Y., & Morstyn, T. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3564889 · Cited by 10
“…can be found in the area of financial portfolio optimization [39], traffic flow optimization [40], drug discovery [41], and power system optimization [42], etc. Reference [43] processes the financial data via quantum RAM and applies the quantum algorithm to speed up the portfolio optimization.”
Ma, H., Lu, Y., Kou, Z., Xue, Z., Yu, W., Zhang, K., Deng, P., Di, C., Zhu, Y., Wang, H., et al. (2025) · Gas Science and Engineering · doi:10.1016/j.jgsce.2025.205744 · Cited by 10
Pre- and post-processing in quantum-computational hydrologic inverse analysis
Golden, J. K., & O’Malley, D. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03115-y · Cited by 10
“Elucidating the challenges of the large k regime, namely that the coefficients in the resulting QUBO covermany orders ofmagnitude, connects the hydrological inverse problemwith other interesting problems featuring large coefficient scales [17,18].”
BackgroundLeenders, L., Sollich, M., Reinert, C., & Bardow, A. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108763 · Cited by 10
“The approach was successfully applied by Ajagekar and You (2019) for solving a heat exchanger network synthesis problem on a quantum annealer.”
MethodDeng, Z., Wang, X., & Dong, B. (2025) · Building Simulation · doi:10.1007/s12273-025-1248-4 · Cited by 9
“Quantum computing can also solve issues in facility location allocation, electric power systems operations, distribution system restoration, and heat exchanger network synthesis (Ajagekar and You 2019; Leenders et al. 2024; Soltaninia and Zhan 2024; Nikmehr et al. 2025).”
Mahmoudi, Y., Zioui, N., Belbachir, H., Tadjine, M., & Rezgui, A. (2022) · Emerging Science Journal · doi:10.28991/esj-2023-07-01-020 · Cited by 10
“In the application of quantum computing to energy system optimization problems, the use of open-source software tools as a first step has been described along with limitations of state-of-the-art quantum computers in this field [99].”
MethodIncentivizing Demand-Side Response Through Discount Scheduling Using Hybrid Quantum Optimization
Bucher, D., Nüßlein, J., O’Meara, C., Angelov, I. B., Wimmer, B., Ghosh, K., Cortiana, G., & Linnhoff–Popien, C. (2024) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2024.3407236 · Cited by 12
“In the literature, there exist multiple QC approaches toward unit commitment [22], [23], [24], [25] and other mixed-integer problems [26], using quantum-inspired alternating direction method of multipliers [27] or Benders decomposition meth-ods [28].”
MethodApplication of Quantum Computing for Power Systems
Li, Y., Venayagamoorthy, G. K., & Du, L. (2025) · Wiley eBooks · doi:10.1002/9781394334599.ch12 · Cited by 9
Geda, M. W., & Tang, Y. M. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100803 · Cited by 8
Jami, A. R., & Haleem, A. (2025) · Human Settlements and Sustainability · doi:10.1016/j.hssust.2025.05.005 · Cited by 11
Quantum Annealing Based Power Grid Partitioning for Parallel Simulation
Hartmann, C., Zhang, J.-J., Calaza, C. D. G., Pesch, T., Michielsen, K., & Benigni, A. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2025.3578243 · Cited by 14
“These include the unit commitment (UC) problem [20], network reconfiguration in distribution grids [21], optimal power flow [22], and the aforementioned [4] grid partitioning into self-sufficient subgrids [23].”
Leveraging quantum science and technology for sustainable development in Africa
Mazhandu, F., & Mafu, M. (2025) · African Journal of Science Technology Innovation and Development · doi:10.1080/20421338.2025.2504259 · Cited by 8
“Quantum technology has demonstrated the ability to contribute to a more sustainable and equitable energy future (Ajagekar and You 2019; Auffèves 2022).”
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
“Ajagekar and You [12] emphasize that quantum computing infrastructure remains economically restrictive, limiting its adoption across industries due to high development costs and energy demands.”
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
Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach
Romero, S. V., Osaba, E., Villar-Rodríguez, E., & Asla, A. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422581 · Cited by 9
“…evolution of related technology and the advances made in its democratization [26] have contributed to the growth of QC community, especially in the optimization field, in which areas as transportation [27], energy [28] or finance [29] have already benefited from this avant-garde computing paradigm.”
BackgroundQuantum technology: a financial risk assessment
Nguyen, P.-N. (2025) · Digital Finance · doi:10.1007/s42521-025-00127-6 · Cited by 12
“These discussions span diverse disciplines, highlighting QC’s potential to solve complex problems in optimization (Ajagekar and You 2019; Wang et al. 2023; Abbas et al. 2024), machine learning (Peral-García et al. 2024; Valdez and Melin 2023; Sajwan et al. 2019; Rane et al. 2024), climate modeling…”
Peace engineering and compassionate operations: a framework for leveraging social good
de Queiroz, M. M., Jabbour, C. J. C., Jabbour, A. B. L. D. S., Pereira, S. C. F., & da Cunha, J. A. C. (2022) · Benchmarking An International Journal · doi:10.1108/bij-01-2022-0021 · Cited by 8
The Impact of Quantum Computing on Businesses
Guarda, T., Torres, W., & Augusto, M. F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10542-5_1 · Cited by 8
Investigating an amplitude amplification-based optimization algorithm for model predictive control
Nieman, K., Durand, H., Patel, S., Koch, D., & Alsing, P. M. (2023) · Digital Chemical Engineering · doi:10.1016/j.dche.2023.100134 · Cited by 8
Chittemma, Y., Teeparthi, K., Naik, R. S., Rao, S. N. V. B., Kumar, Y. V. P., & Mallipeddi, R. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110779 · Cited by 7
Quantum computing for production planning
Riandari, F., Alesha, A., & Sihotang, H. T. (2021) · International Journal of Enterprise Modelling · doi:10.35335/emod.v15i3.50 · Cited by 7
“In addition, integrating quantum computing into existing production planning systems and infrastructure presents integration difficulties(Ajagekar & You, 2019)(Fuchigami & Rangel, 2018)(X. Li & Gao, 2020).”
BackgroundQuantum Technologies in Industry 4.0: Navigating the Ethical Frontier with Value-Sensitive Design
Umbrello, S. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.01.163 · Cited by 9
“This in turn leads to concerns regarding the long-term viability of such systems and their environmental effects [16].”
BackgroundApplying Quantum Computing to Solve Multicommodity Network Flow Problem
Niu, C., Rastogi, P., Soman, J. T., Tamuli, K., & Dixit, V. V. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3532640 · Cited by 10
“Currently, there are more studies investigating the computing capability of the quantum computing in different fields such as transportation [12], [17], [18], [19], engineering design [20], finance [21], energy system [22].”
Quantum Computing in Civil Engineering: Potentials and Limitations
Ploennigs, J., Berger, M., Mevissen, M., & Smarsly, K. (2025) · Lecture Notes in Civil Engineering · doi:10.1007/978-3-031-87364-5_2 · Cited by 7
Discrete optimal control of building to grid integration based on quantum computing
Deng, Z., & Dong, B. (2023) · Building Simulation Conference proceedings · doi:10.26868/25222708.2023.1545 · Cited by 6
“…been employed for real-time control of building HVAC systems (Deng et al, 2023) and solving problems related to facility location allocation for energy system infrastructure development, electric power systems operations unit commitment, and heat exchanger network synthesis (Ajagekar et al, 2019).”
MethodChieza, H. A., Khumalo, M. T., Prag, K., & Woolway, M. (2020) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi51676.2020.9311605 · Cited by 7
“Importantly, however, the computational results presented herein extend and agree with other findings in the quantum computing literature [7, 8].”
MethodResultHybrid quantum-classical multi-cut Benders approach with a power system application
Paterakis, N. G. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108161 · Cited by 6
“However, the problem formulations presented in [29] could either be directly recast as QUBO problems or discretization of continuous variables was performed, at the expense of using a large number of ancillary qubits in order to represent the discretized variables.”
MethodOn energy resilience and energy vulnerability measurement
Gatto, A., & Drago, C. (2023) · Environment Development and Sustainability · doi:10.1007/s10668-023-03222-z · Cited by 6
Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning
Ju, K., Qin, X., Zhong, H., Zhang, X., Pan, M., & Liu, B. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622663 · Cited by 6
“Based on the advance of these technologies, quantum computing is becoming a powerful alternative to conduct computing intensive machine learning or optimization tasks [1, 2].”
MethodFuturistic Technologies for Smart Manufacturing
Tyagi, A. K., Mishra, A. K., Vedavathi, N., Kakulapati, V., & Sajidha, S. A. (2023) · Wiley eBooks · doi:10.1002/9781394213948.ch21 · Cited by 6
Physics informed quantum computing: A decade scientometric analysis
Sood, V., & Chauhan, R. P. (2024) · Journal of Computational Science · doi:10.1016/j.jocs.2024.102382 · Cited by 7
Quantum Computing for Complex Energy Systems: A Review
Heinen, X., & Chen, H. (2024) · International Conference on Data-driven Optimization of Complex Systems (DOCS) · doi:10.1109/docs63458.2024.10704424 · Cited by 5
Ajagekar, A., & You, F. (2025) · Engineering · doi:10.1016/j.eng.2025.02.002 · Cited by 6
Self-citationExploring Hybrid Quantum-Classical Algorithms for Multiscale Bioprocess Optimization
Hernández-Romero, I. M., Gibford, S. M., Clements, W. R., Savoie, C. J., Flores‐Tlacuahuac, A., & Mansouri, S. S. (2025) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c04624 · Cited by 5
A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem
Aboumrad, W., Marthi, P. R. V., Debnath, S., Roetteler, M., & Epifanovsky, E. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00208 · Cited by 5
“UC is one of the power system optimization problems that has been researched using multiple quantum computing algorithms [6], [7], [8], [9].”
A Quantum Approach for Tactical Capacity Management of Distributed Electricity Generation
Phillipson, F., & Chiscop, I. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-06668-9_23 · Cited by 5
DC Optimal Power Flow in Unit Commitment Using Quantum Computing: An ADMM Approach
Magar, M. R., Pandit, D., Nguyen, D., & Nguyen, N. (2024) · North American Power Symposium (NAPS) · doi:10.1109/naps61145.2024.10741684 · Cited by 5
“Although quantum computing can outperform classical methods for solving optimization, technological barriers in hardware and software need to be overcome to unlock the full potential [5].”
MethodA Hybrid Quantum-Classical Machine Learning Approach to Offshore Wind Farm Power Forecasting
Hangun, B., Akpinar, E., Oduncuoğlu, M., Altun, O., & Eyecioğlu, Ö. (2024) · International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera62673.2024.10815339 · Cited by 5
Quantum Computing Approach for Energy Optimization in a Prosumer Community
Mastroianni, C., Scarcello, L., & Settino, J. (2022) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9927770 · Cited by 5
Review of Applications of Quantum Computing in Power Flow Calculation
Jang, Y.-E., Kim, N.-Y., & Kim, Y. (2024) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-024-01804-z · Cited by 5
“However, quantum effects start to degrade the functionality of classical transistors, due to the size reduction (e.g., to a few atoms thick) [13].”
InfluentialDecoherence dynamics estimation for superconducting gate-model quantum computers
Gyöngyösi, L. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-02863-7 · Cited by 6
A Leap among Entanglement and Neural Networks: A Quantum Survey.Preprint
Massoli, F. V., Vadicamo, L., Amato, G. M., & Falchi, F. (2021) · arXiv (Cornell University) · Cited by 5
Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges
Kim, S., Suh, I., Humble, T. S., Beck, T. L., lee, E., & Luo, T. (2026) · ACS Energy Letters · doi:10.1021/acsenergylett.5c04009 · Cited by 6
“16 − 69 Hamiltonian Simulation QC has shown promise in addressing challenging problems in materials science by directly encoding the quantum nature of molecular systems into quantum circuits.”
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. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111153 · Cited by 5
Quantum gate control pulse optimization based on the Adam algorithm
Yang, M., Feng, Y., Lu, B., Zhao, H., Ma, G. L., & Wang, L. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04791-w · Cited by 4
“On the basis of the GRAPE algorithm, second-order optimization meth-ods such as the BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm have been introduced to accelerate convergence and improve optimization stability [23, 24].”
Hybrid quantum-classical optimization of the resource scheduling problem
Christeson, T., Ullah, M. H., Arabnya, A., Khodaei, A., & Fan, R. (2026) · Next research. · doi:10.1016/j.nexres.2026.101350 · Cited by 4
From Classical to Quantum: A Review of Recent Progress in Reinforcement Learning
Kashyap, K., Lalit, Shah, D., & Gautam, L. (2021) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet51464.2021.9456218 · Cited by 4
Dharmawati, T., Judijanto, L., Fatmawati, E., Rokhim, A., Ruhana, F., & Erkamim, M. (2023) · ICST Transactions on Scalable Information Systems · doi:10.4108/eetsis.4373 · Cited by 4
“There is no indication of which answer is more likely to be correct: giving answers randomly is no different from checking them in a certain order (Ajagekar & You, 2019).”
BackgroundNew Trends for Machine Learning Applications in Future Power Systems
Amiri, M. M., Jalilian, A., Abdi, H., Rezaei, M., & Nazari‐Heris, M. (2024) · Power systems · doi:10.1007/978-3-031-69358-8_4 · Cited by 4
Quantum computing for molecular vibrational energies: A comprehensive study
Somasundaram, R., Jayaharish, R., Ramanan, R., & Chowdhury, C. (2025) · Materials Today Quantum · doi:10.1016/j.mtquan.2025.100031 · Cited by 5
Development of Evolutionary Systems Based on Quantum Petri Nets
Letia, T. S., Durla-Pasca, E. M., Al-Janabi, D., & Cuibus, O. (2022) · Mathematics · doi:10.3390/math10234404 · Cited by 4
A Quantum Approach to the Problem of Charging Electric Cars on a Motorway
Różycki, R., Józefowska, J., Kurowski, K., Lemański, T., Pecyna, T., Subocz, M., & Waligóra, G. (2022) · Energies · doi:10.3390/en16010442 · Cited by 4
Distributed quantum computation for near-term quantum environments
Gyöngyösi, L., & Imre, S. (2021) · doi:10.1117/12.2585996 · Cited by 3
The Second Quantum Revolution: Unexplored Facts and Latest News
Intonti, K., Viscardi, L., Lamberti, V., Matteucci, A., Micciola, B., Modestino, M. R., & Noce, C. (2024) · Encyclopedia · doi:10.3390/encyclopedia4020040 · Cited by 8
“Grid Optimization: Quantum algorithms can be employed to optimize the operation and control of power grids, thus ensuring efficient energy distribution and minimizing transmission losses [108].”
MethodQUIRCY: QUantum Integrated ResilienCY for Power Systems
Salman, U. T., Wang, Z., & Hansen, T. M. (2025) · IEEE Access · doi:10.1109/access.2025.3592830 · Cited by 5
Tripathi, S., Misra, R., & Singh, T. N. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108096 · Cited by 3
Earth Grid: Toward a low-carbon energy infrastructure
Kumar, A., He, X., Deng, Y., Sah, B., Singh, A. R., Kumar, P., Bansal, R. C., & Kirtley, J. L. (2025) · iScience · doi:10.1016/j.isci.2025.113681 · Cited by 3
“73,74 79 Among all these technologies, quantum computing stands out as the least developed.”
Quantum computing approach for building surface sunlit in urban-scale energy modeling
Deng, Z., Xu, Y., & Hong, T. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116898 · Cited by 3
Hybrid Quantum-Classical Algorithm for an Integrated Feature Selection and Logistic Regression Model
Ye, L., Wen, M., Zhang, F., Xu, Y., Zhang, Y., Ma, Y., & Wen, K. (2026) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2026.3657976 · Cited by 3
“Existingstudies predominantlyemploystaticdiscretizationstrategies(e.g.,given discretizationsteplengthsdirectly)forcontinuousdecision variablesinQUBOformulations[42].”
Energy supply scheduling in manufacturing systems using Quantum Annealing
Schworm, P., Wu, X., Wagner, M., Ehmsen, S., Glatt, M. F., & Aurich, J. C. (2023) · Manufacturing Letters · doi:10.1016/j.mfglet.2023.09.005 · Cited by 3
Quantum Optimization in Engineering: A Brief Review
Mahmoudi, Y., Zioui, N., & Belbachir, H. (2023) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa59624.2023.10286779 · Cited by 3
“ALGORITHMS PROBLEMS Ajagekar and You (2019) have examined the possibility of using open-source software tools as a first preliminary step in applying quantum computing to optimization problems concerning energy systems, as well as the restrictions of high-tech quantum computers in this field [41].”
BackgroundLearning to Optimize Quantum Neural Networks Without Gradients
Kulshrestha, A., Liu, X., Ushijima‐Mwesigwa, H., & Safro, I. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00037 · Cited by 7
“…current state of quantum machines is still far from being practically competitive to the classical counterparts, the emergent field of Quantum Machine Learning (QML) is an important step towards the future in which quantum computers form the basis of many challenging computational tasks [1], [2].”
BackgroundZhu, J., Wang, X., Wang, Z., & Xu, Y. (2024) · Structures · doi:10.1016/j.istruc.2024.108086 · Cited by 3
Kumar, D., Chauhan, Y. K., & Pandey, A. S. (2025) · Sustainability · doi:10.3390/su17083734 · Cited by 3
Quantum Computing for Cable-Routing Problem in Solar Power Plants
Zhao, Z., Fan, L., Zheng, H., & Han, Z. (2023) · North American Power Symposium (NAPS) · doi:10.1109/naps58826.2023.10318615 · Cited by 3
“[10] discusses the challenges and opportunities in the fields of energy systems, and [11] points out the quantum computing speed-up potential in renewable energy topic.”
BackgroundExperimental Demonstration of Fermionic QAOA with One-Dimensional Cyclic Driver Hamiltonian
Yoshioka, T., Sasada, K., Nakano, Y., & Fujii, K. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00041 · Cited by 3
Quantum computer-aided job scheduling for storage and retrieval systems
Windmann, S. (2024) · at - Automatisierungstechnik · doi:10.1515/auto-2023-0072 · Cited by 3
Quantum- Inspired Optimal Power Flow
Amani, F., & Kargarian, A. (2024) · IEEE Texas Power and Energy Conference (TPEC) · doi:10.1109/tpec60005.2024.10472285 · Cited by 3
“A CCURACY and speed of the mathematical methodologies in addressing complex engineering problems have consistently been given significant attention [1], [2].”
Background𝐶𝑙𝑎𝑠𝑠𝑖|𝑄⟩: Towards a Translation Framework to Bridge the Classical-Quantum Programming Gap
Esposito, M., Sabzevari, M. T., Ye, B., Falessi, D., Khan, A. A., & Taibi, D. (2024) · doi:10.1145/3663531.3664752 · Cited by 3
“Since Quantum Computing has reached the so-called quantum supremacy [20], the race to “ quantumize the algorithm ” is hastened by industry and research in every field [1, 2, 23, 25, 28, 31–33].”
Magic Mirror on the Wall, How to Benchmark Quantum Error Correction Codes, Overall?
Chatterjee, A., & Ghosh, S. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00050 · Cited by 7
“It is based on classical error-correcting codes, specifically the [7,4,3] Hamming code, which corrects single-bit errors in classical computing.”
Energy integration and optimisation for sustainable total site, process and equipment design
Walmsley, T. G., Varbanov, P. S., Aviso, K. B., & You, F. (2019) · Energy · doi:10.1016/j.energy.2019.115896 · Cited by 3
Self-citationQuantum annealing for nearest neighbour compliance problem
Müller, S., & Phillipson, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-73882-y · Cited by 2
“However, Quantum Annealing has been applied to a variety of relevant problems in recent years, including the Nurse Scheduling Problem 19 , Traffic Flow Optimisation 20 , Portfolio Optimisation 21 , Energy System Optimisation 22,23 , and Job Scheduling 24 .”
MethodModeling Electricity Markets and Energy Systems: Challenges and Opportunities
Aliabadi, D. E., & Pinto, T. D. O. (2025) · Energies · doi:10.3390/en18020245 · Cited by 2
“The borderland separating the power market and energy system models is vanishing as computing power proliferates at a considerable rate, which enables the next generation of energy system models to consider operational complexity with high temporal and spatial detail [27,28].”
How might quantum computing impact climate change and the wider environment?
Theophilo, K., & Oughton, N. (2025) · Research Directions Quantum Technologies · doi:10.1017/qut.2025.1 · Cited by 2
“…simulation to discover novel materials and improve battery technologies (Arenas et al., 2019; Sánchez-Díez et al., 2021; Rice et al., 2021; Delgado et al., 2022), and quantum optimisation to more efficiently balance the energy grid (Ajagekar and You, 2019; Yang et al., 2020; Zhou et al., 2022).”
Quantum logic gate analogies in nonlinear acoustics
Kuk, I., Djordjević, I. B., Gabitov, I. R., Runge, K., Ige, A. S., & Deymier, P. A. (2025) · The Journal of the Acoustical Society of America · doi:10.1121/10.0036901 · Cited by 2
Kansara, R., Kyriakidis, L., & Serrano, M. I. R. (2025) · Energies · doi:10.3390/en18164258 · Cited by 2
Digitalization and environmental sustainability ramifications on the Moroccan companies’ resilience
Gouiferda, F., & Iddik, S. (2025) · Management & Sustainability An Arab Review · doi:10.1108/msar-02-2025-0074 · Cited by 2
Quantum Approaches for the Unit Commitment Problem - a Literature Survey
Phillipson, F., & Muller, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-13855-2_1 · Cited by 2
Saha, N., Bose, R., Roy, S., Sutradhar, S., Das, S., Khan, A. A., Ali, F., AlZubi, A. A., & Alanazi, J. M. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3597 · Cited by 2
Quantum computing for smart grid
Lin, F., Wang, Z., Ren, C., Li, X., Rodriguez-Andina, J. J., Vázquez, S., Mantooth, H. A., Skoglund, M., van der Laan, T., & Usman, M. (2026) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-026-00295-6 · Cited by 2
“Early mappings on quantum annealers showed that discretizing continuous outputs inflates the number of binary variables substantially, producing solutions close to the classical optimum but more sensitive to hardware noise than the classical solutions they approximate 70 .”
An Overview of Quantum Computing Approach in the Present-Day Energy Systems
Biswas, C., Pal, J., & Majumder, S. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-2412-5_3 · Cited by 2
Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
Chatterjee, A., Das, S. K., & Ghosh, S. (2025) · Applied Sciences · doi:10.3390/app15084555 · Cited by 3
Quantum Computing Algorithms for Nonlinear Optimization Problems
Vishwakarma, V. K. (2023) · Communications on Applied Nonlinear Analysis · doi:10.52783/cana.v30.279 · Cited by 3
Ajagekar, A., & You, F. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.07.480 · Cited by 2
Self-citationShaheen, O., Elshazly, O., Baihan, A., El‐Shafai, W., & Khalil, H. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.09.066 · Cited by 2
Exploring quantum systems for pseudo-random number generation
Cruz, L. J. M. S., Faina, L. F., & Pereira, J. H. D. S. (2024) · Quantum Studies Mathematics and Foundations · doi:10.1007/s40509-024-00348-1 · Cited by 2
“Finally, the paper introduces and details the Quantum Quasi-Probability Pseudo-Random Number Generator (QQ-PRNG) algorithm for generating pseudo-random numbers.”
Quantum-Compatible Unit Commitment Modeling through Logarithmic Discretization
Christeson, T., Khodaei, A., & Eskandarpour, R. (2024) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope62998.2024.10863738 · Cited by 3
“P ROBLEM F ORMULATION The BQM formulation found in [4] for a single time step is referred to as Approach I , and the BQM formulation for a single time step with the new discretization strategy proposed in this paper is referred to as Approach II .”
InfluentialHong, Y., & Santos, J. B. D. (2025) · Energies · doi:10.3390/en18071771 · Cited by 2
Alonso, M., Martínez, R., Díez-Valle, P., Garbayo, A., García‐Santiago, X., & Gil, G. B. (2025) · IEEE Access · doi:10.1109/access.2025.3578780 · Cited by 2
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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