Quantum-Enhanced Optimization: Bridging AI and Next-Generation Computing

DOI

10.2991/978-94-6463-787-8_48How to use a DOI?

Keywords

Quantum Computing; Machine Learning; High-Dimensional Optimization; Hybrid Quantum-Classical Systems; Sustainable Computing

Abstract

Quantum computing and artificial intelligence (AI) are positioned as groundbreaking tools capable of tackling complexities inherent to modern computation posed by high-dimensional optimization problems. This research presents a quantum-enhanced optimization framework that leverages the QAOA (Quantum Approximate Optimization Algorithm) and VQE (Variational Quantum Eigen solver) within a hybrid quantum-classical paradigm. Machine learning models, including reinforcement learning and NN (neural networks), are integrated to optimize the quantum circuit parameters, enhance convergence, and improve scalability for complex problem spaces.

Experimental evaluations demonstrate that this approach outperforms traditional optimization techniques in terms of efficiency and solution accuracy, particularly for large-scale multi-objective tasks. The framework is applied to key domains such as smart energy systems, logistics, and scientific modeling, illustrating its versatility and alignment with sustainable development goals. This study also explores the limitations of current quantum hardware and discusses future pathways for advancing hybrid quantum AI systems, offering significant contributions to next generation computational methodologies and intelligent problem solving.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Anil Kumar Jonnalagadda
AU  - Praveen Kumar Myakala
PY  - 2025
DA  - 2025/07/17
TI  - Quantum-Enhanced Optimization: Bridging AI and Next-Generation Computing
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 623
EP  - 633
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_48
DO  - 10.2991/978-94-6463-787-8_48
ID  - Jonnalagadda2025
ER  -