Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing
The rapid advancement of Artificial Intelligence (AI) is reshaping industries and driving global innovation. However, the increasing complexity of AI models demands substantial data and computational resources, leading to significant energy consumption and environmental impact. This article explores...
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| Format: | Article |
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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/10937702/ |
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| author | Andreas Andreou Constandinos X. Mavromoustakis Evangelos K. Markakis Athina Bourdena George Mastorakis |
| author_facet | Andreas Andreou Constandinos X. Mavromoustakis Evangelos K. Markakis Athina Bourdena George Mastorakis |
| author_sort | Andreas Andreou |
| collection | DOAJ |
| description | The rapid advancement of Artificial Intelligence (AI) is reshaping industries and driving global innovation. However, the increasing complexity of AI models demands substantial data and computational resources, leading to significant energy consumption and environmental impact. This article explores the integration of quantum computing and end-to-end automation strategies in cloud-edge architectures. It proposes a hybrid quantum-classical AI framework that enhances training efficiency and reduces data and processing intensity by minimizing energy consumption. The framework leverages automated model orchestration, adaptive resource allocation, and intelligent data processing at the edge to improve system efficiency. In addition, it addresses ethical considerations, including privacy, fairness, and trustworthiness, to ensure alignment with human values. This approach significantly improves AI performance while fostering a sustainable and ethical AI ecosystem. |
| format | Article |
| id | doaj-art-841a3fa7a5314c42a77b4608bfb5d99b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-841a3fa7a5314c42a77b4608bfb5d99b2025-08-20T01:55:02ZengIEEEIEEE Access2169-35362025-01-0113546225463510.1109/ACCESS.2025.355402410937702Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge ComputingAndreas Andreou0https://orcid.org/0000-0002-9432-916XConstandinos X. Mavromoustakis1https://orcid.org/0000-0003-0333-8034Evangelos K. Markakis2https://orcid.org/0000-0003-0959-598XAthina Bourdena3https://orcid.org/0009-0008-2049-3910George Mastorakis4https://orcid.org/0000-0002-6733-5652Department of Computer Science, University of Nicosia, Nicosia, CyprusDepartment of Computer Science, University of Nicosia, Nicosia, CyprusDepartment of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Crete, GreeceDepartment of Business Administration and Tourism, Hellenic Mediterranean University, Heraklion, Crete, GreeceDepartment of Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, Crete, GreeceThe rapid advancement of Artificial Intelligence (AI) is reshaping industries and driving global innovation. However, the increasing complexity of AI models demands substantial data and computational resources, leading to significant energy consumption and environmental impact. This article explores the integration of quantum computing and end-to-end automation strategies in cloud-edge architectures. It proposes a hybrid quantum-classical AI framework that enhances training efficiency and reduces data and processing intensity by minimizing energy consumption. The framework leverages automated model orchestration, adaptive resource allocation, and intelligent data processing at the edge to improve system efficiency. In addition, it addresses ethical considerations, including privacy, fairness, and trustworthiness, to ensure alignment with human values. This approach significantly improves AI performance while fostering a sustainable and ethical AI ecosystem.https://ieeexplore.ieee.org/document/10937702/Sustainable AIquantum-inspired optimizationcloud-edge computingautomationethical AIenergy efficiency |
| spellingShingle | Andreas Andreou Constandinos X. Mavromoustakis Evangelos K. Markakis Athina Bourdena George Mastorakis Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing IEEE Access Sustainable AI quantum-inspired optimization cloud-edge computing automation ethical AI energy efficiency |
| title | Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing |
| title_full | Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing |
| title_fullStr | Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing |
| title_full_unstemmed | Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing |
| title_short | Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing |
| title_sort | sustainable ai with quantum inspired optimization enabling end to end automation in cloud edge computing |
| topic | Sustainable AI quantum-inspired optimization cloud-edge computing automation ethical AI energy efficiency |
| url | https://ieeexplore.ieee.org/document/10937702/ |
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