Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor
This work explores the use of variational quantum algorithms to optimize energy distribution among users in energy communities, using real data from a community lab. This requires integrating various energy sources, storage solutions, and the ability to respond to variations in demand within energy...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11030452/ |
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| author | Mateo Alonso Guillermo Rubinos Rodriguez Pablo Diez-Valle Ana Garbayo Xela Garcia-Santiago Gonzalo Blazquez Gil |
| author_facet | Mateo Alonso Guillermo Rubinos Rodriguez Pablo Diez-Valle Ana Garbayo Xela Garcia-Santiago Gonzalo Blazquez Gil |
| author_sort | Mateo Alonso |
| collection | DOAJ |
| description | This work explores the use of variational quantum algorithms to optimize energy distribution among users in energy communities, using real data from a community lab. This requires integrating various energy sources, storage solutions, and the ability to respond to variations in demand within energy systems, while maintaining the capacity to adapt to the variability of renewable energy sources. Given the increasing complexity as the problem size grows and the limitations of classical computing methods in addressing it, we study an approach that models the problem using QUBO and solves it applying quantum optimization algorithms. Our analysis includes a comparative study of two variational techniques, QAOA and VQE, and the implementation of the former on Qmio, an actual superconducting quantum processor. Although deploying QAOA on Qmio required specific adaptations to the hardware, we were able to achieve a shift of the probability distribution towards lower-energy solutions without error mitigation techniques, which highlights both QAOA’s potential and the intrinsic challenges of quantum optimization. |
| format | Article |
| id | doaj-art-12022617ffb24f41b05bde63bcd5092f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-12022617ffb24f41b05bde63bcd5092f2025-08-20T03:23:59ZengIEEEIEEE Access2169-35362025-01-011310614010615410.1109/ACCESS.2025.357878011030452Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting ProcessorMateo Alonso0https://orcid.org/0009-0007-6407-7655Guillermo Rubinos Rodriguez1Pablo Diez-Valle2Ana Garbayo3https://orcid.org/0000-0003-2214-2555Xela Garcia-Santiago4https://orcid.org/0000-0001-9799-5179Gonzalo Blazquez Gil5https://orcid.org/0000-0001-6652-5975Instituto Tecnológico de Galicia, A Coruña, SpainInstituto Tecnológico de Galicia, A Coruña, SpainInstituto Tecnológico de Galicia, A Coruña, SpainInstituto Tecnológico de Galicia, A Coruña, SpainInstituto Tecnológico de Galicia, A Coruña, SpainInstituto Tecnológico de Galicia, A Coruña, SpainThis work explores the use of variational quantum algorithms to optimize energy distribution among users in energy communities, using real data from a community lab. This requires integrating various energy sources, storage solutions, and the ability to respond to variations in demand within energy systems, while maintaining the capacity to adapt to the variability of renewable energy sources. Given the increasing complexity as the problem size grows and the limitations of classical computing methods in addressing it, we study an approach that models the problem using QUBO and solves it applying quantum optimization algorithms. Our analysis includes a comparative study of two variational techniques, QAOA and VQE, and the implementation of the former on Qmio, an actual superconducting quantum processor. Although deploying QAOA on Qmio required specific adaptations to the hardware, we were able to achieve a shift of the probability distribution towards lower-energy solutions without error mitigation techniques, which highlights both QAOA’s potential and the intrinsic challenges of quantum optimization.https://ieeexplore.ieee.org/document/11030452/Energy communitiesenergy efficiencyoptimization modelsquantum algorithmsquantum computingquantum optimization |
| spellingShingle | Mateo Alonso Guillermo Rubinos Rodriguez Pablo Diez-Valle Ana Garbayo Xela Garcia-Santiago Gonzalo Blazquez Gil Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor IEEE Access Energy communities energy efficiency optimization models quantum algorithms quantum computing quantum optimization |
| title | Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor |
| title_full | Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor |
| title_fullStr | Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor |
| title_full_unstemmed | Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor |
| title_short | Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor |
| title_sort | modeling energy communities a case study of quantum approximate optimization on a superconducting processor |
| topic | Energy communities energy efficiency optimization models quantum algorithms quantum computing quantum optimization |
| url | https://ieeexplore.ieee.org/document/11030452/ |
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