Variational optimization in the AI era

Dmitrii Kochkov, Bryan K. Clark

Osama M. Raisuddin, Suvranu De

Arunkumar Medisetty

Businesses are confronting a data deluge in volume and complexity that is bearing on traditional data engineering and decisionmaking 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.

Maria Schuld, Francesco Petruccione

Syed Muntasir Mamun

Quantum-AI hybrid algorithms represent the most practical near-term pathway for leveraging quantum computing's probabilistic power alongside classical AI's scalability and optimization strengths. In the noisy intermediate-scale quantum (NISQ) era, these hybrids—primarily variational quantum algorithms (VQAs) such as the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA)—delegate hard subproblems (e.g., high-dimensional sampling, optimization under quantum constraints, or feature extraction in entangled spaces) to quantum processors while classical optimizers or neural networks handle parameter tuning, error mitigation, and large-scale data processing (Ferguson et al., 2026; Acampora, 2026). This exploration builds on the 2025 NSF AI+MPS community paper by integrating accelerated timelines: mass deployment of utility-scale quantum computing by August 2027 and AGI substantially before 2030. Hybrid approaches become critical enablers for MPS grand challenges in Mamun, SM (2026)…………………………………………1 Quantum-AI Hybrid Algorithms: Bridging Classical Intelligence, Quantum Acceleration, and Scientific Discovery in the MPS Domains (Vol-I) chemistry (molecular ground states), materials (inverse design), physics (many-body simulation), and astronomy (anomaly detection under uncertainty). Also available on Academia.edu: https://www.academia.edu/attachments/132469868/download_file?s=portfolio