Quantum-Enhanced Agentic AI: A New Frontier for Decision Intelligence in Large-Scale Data Engineering

DOI

10.2991/978-94-6463-978-0_21How to use a DOI?

Keywords

Quantum Computing; Agentic AI; Data Engineering; Large-Scale Systems; Decision Intelligence; Combinatorial Optimization; Retrieval-Augmented Generation (RAG); Context-Augmented Generation (CAG)

Abstract

Businesses are confronting a data deluge in volume and complexity that is bearing on traditional data engineering and decision making models to the beyond. The Quantum-Enhanced Data Engineering Agent Framework (QED-AF) resource offered in this paper is a new framework that combines the power of autonomous reasoning of Agentic AI with the capabilities of quantum computing, obtained through the application of quantum computers. We overcome a complicational problem of optimization that is in most cases unsolvable in classical systems: combinatorial optimization of large scale data operations. The framework uses two major AI methods to contextual knowledge: Context- Augmented Generation (CAG) to receive real-time environmental awareness and Retrieval-Augmented Generation (RAG) to be guided by the past and best practices. The main idea is the Quantum Variational Optimization (QVO) algorithm, a quantum-classical process in which the agent modeling complex optimization problems (e.g., resource allocation, query optimization) is used as a quantum backend. QED-AF will achieve a radical increase in the quality and efficiency of decisions by using quantum parallelism to search large solution space. Simulations of a complex ETL resource allocation scenario demonstrate that the QVO solution is up to 35% more optimal than the most advanced classical heuristics, which ushers a new era of smarter, quantum-accelerated data platforms.

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  - Arunkumar Medisetty
PY  - 2025
DA  - 2025/12/31
TI  - Quantum-Enhanced Agentic AI: A New Frontier for Decision Intelligence in Large-Scale Data Engineering
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 230
EP  - 238
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_21
DO  - 10.2991/978-94-6463-978-0_21
ID  - Medisetty2025
ER  -