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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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“Quantum computers are expected to be more energy efficient as compared to supercomputers while doing a particular task [28].”
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“• Green energy and resource planning (Ajagekar & You, 2019).”
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“Although it has been shown that some optimization problems relevant to energy systems can be solved using quantum computers, their performance deteriorates with increasing size and complexity [17].”
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Quantum computing for smart grid applications
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Annealing-Based Quantum Computing for Combinatorial Optimal Power Flow
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“In [42], quantum annealing is demonstrated for generator unit commitment, but power flow modelling and network constraints are not considered.”
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Chemical data intelligence for sustainable chemistry
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Ajagekar, A., & You, F. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.107119 · Cited by 79
“( ) 1 data model t t j j j j c c h h α ε + = + − (7)”
Self-citationHybrid quantum-classical reservoir computing of thermal convection flow
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Synergistic Swarm Optimization Algorithm
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Circuit Depth Reduction for Gate-Model Quantum Computers
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Archives of Quantum Computing: Research Progress and Challenges
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“Indeed, quantum computers are expected to be more energy-efficient than supercomputers considering Reference [10].”
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Deng, Z., Wang, X., & Dong, B. (2023) · Applied Energy · doi:10.1016/j.apenergy.2022.120621 · Cited by 59
Ajagekar, A., Hamoud, K. A., & You, F. (2022) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2022.3187367 · Cited by 50
Self-citationToward Resilient Modern Power Systems: From Single-Domain to Cross-Domain Resilience Enhancement
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“Mollah et al. [172], Zhou et al. [173], Ajagekar and You [174], Ullah et al. [175], Di Silvestre et al. [176], and Mohammadi and Saif [177] have summarized the applications, development, limitations, and directions of quantum computing and blockchain technologies in power systems.”
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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
“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 51
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Advanced Modeling and Optimization Strategies for Process Synthesis
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Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community
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“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].”
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Self-citationNew frontiers of quantum computing in chemical engineering
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“Unfortunately, large processing power is required to accomplish such optimization on a large scale [19], including power generation, transmission, and distribution [20].”
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Self-citationQuantum computing: Impact on energy efficiency and sustainability
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Quantum computing basics, applications and future perspectives
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“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
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“, wind turbine placement [58], hybrid energy system design [59, 60], and in automotive industry applications [61].”
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Deep learning-enhanced quantum optimization for integrated job scheduling in container terminals
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Integration of planning, scheduling, and control: A review and new perspectives
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Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning
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“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].”
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“…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 38
“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 32
Routing space exploration for scalable routing in the quantum Internet
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Quantum-Computing-Inspired Optimal Power Allocation Mechanism in Edge Computing Environment
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“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 35
Building Knowledge Capacity for Quantum Computing in Engineering Education
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“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
Dynamics of entangled networks of the quantum Internet
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“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 28
“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
Santos, V. O., Marinho, F. P., Rocha, P. A. C., Thé, J. V. G., & Gharabaghi, B. (2024) · Energies · doi:10.3390/en17143580 · Cited by 24
“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
“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
Experimental Study of the Optical Qubit on the 435-nm Quadrupole Transition in the 171Yb+ Ion
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Advances in Virtual Power Plant Operations: A Review of Optimization Models
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“…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
“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
“Recently, quantum computing has been applied to problems in process systems engineering such as optimization and machine learning (Ajagekar et al. (2020); Ajagekar and You (2019)).”
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Gyöngyösi, L., & Imre, S. (2025) · Quantum Information and Computation · doi:10.2478/qic-2025-0006 · Cited by 20
Quantum Hardware Devices (QHDs): Opportunities and Challenges
Oukaira, A. (2025) · IEEE Access · doi:10.1109/access.2025.3576216 · Cited by 22
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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 18
“The targets are combinatorial optimization problems in industry, such as financial optimization [1, 2], logistics and distribution optimization [3, 4], and energy optimization [5].”
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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
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“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
“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
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
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
“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 24
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
Kundu, S., Gupta, T., Sardar, A., Bandyopadhyay, A., Swain, S., & Mallik, S. (2025) · Franklin Open · doi:10.1016/j.fraope.2025.100371 · Cited by 21
Quantum computing-enhanced large-scale residential electric vehicle charging management
Deng, Z., Li, Y., Wang, X., Jiang, Z., & Dong, B. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126772 · Cited by 13
Quantum-Enhanced Machine Learning
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
Chen, W. J., Marcus, G. S., & Leesburg, D. (2021) · International Journal of Enterprise Modelling · doi:10.35335/emod.v15i3.48 · Cited by 16
“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 14
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 20
“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 14
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 12
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
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 14
“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
“…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
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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