Published: 2025-08-01
Hybrid Quantum-Classical Optimization for Energy-Efficient Large Language Models
DOI: 10.35870/ijsecs.v5i2.5099
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Abstract
The rapid evolution of Large Language Models (LLMs) has transformed natural language processing, enabling sophisticated applications across various sectors. However, the substantial computational demands associated with training and deploying LLMs result in significant energy consumption and carbon emissions. This study introduces an optimized hybrid quantum-classical framework that integrates variational quantum algorithms (VQAs) with accelerated classical learning techniques. By harnessing quantum computing for complex non-linear optimization and employing prompt learning to minimize full model retraining, the proposed approach enhances both computational efficiency and sustainability. Simulation outcomes indicate that the hybrid method can reduce energy usage by up to 30% and shorten computation time by 25% relative to conventional classical approaches, without diminishing model accuracy. These improvements are substantiated through quantitative analysis and visualized energy metrics. The adaptability of the framework supports its application in diverse areas, including sustainable energy management, supply chain optimization, and environmentally conscious transportation systems. Nevertheless, the broader implementation of such hybrid solutions remains constrained by current quantum hardware capabilities and integration challenges with classical infrastructure. The findings underscore the potential of hybrid quantum-classical optimization as a pathway toward sustainable AI development. Future research should prioritize advancements in quantum hardware reliability and interdisciplinary collaboration to accelerate practical adoption, thereby supporting global efforts in energy efficiency and environmental responsibility.
Keywords
Large Language Models ; Hybrid Quantum-Classical ; Variational Quantum Algorithms ; Energy Efficiency ; Sustainable AI ; Carbon Emissions ; Prompt Learning
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 5 No. 2 (2025)
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Section: Articles
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/ijsecs.v5i2.5099
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Loso Judijanto
IPOSS Jakarta Indonesia, South Jakarta City, Special Capital Region of Jakarta, Indonesia
Yuswardi Yuswardi
Department of Informatics Engineering, Universitas Jabal Ghafur, Pidie Regency, Aceh Province, Indonesia
Fitriyani Fitriyani
Department D3 Informatics Engineering, Universitas Jabal Ghafur, Pidie Regency, Aceh Province, Indonesia
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