Hybrid Quantum-Classical Modeling Framework for Multiscale and Multiphysics Optimization of Nanotechnology-Driven Advanced Energy Systems

Recent developments in quantum computing have triggered a paradigm shift extending from algorithmic theory to applied engineering fields, particularly in the modeling and optimization of complex energy systems. This study presents a hybrid quantum-classical computational framework designed for the multiscale and multiphysics modeling of nanotechnology-driven energy systems. The illustrative case involves a proton exchange membrane fuel cell (PEMFC) utilizing nanostructured materials, governed by electrochemical reactions, transport phenomena, degradation mechanisms, and adaptive control dynamics. The proposed framework integrates classical numerical solvers with quantum algorithms such as the Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), and Harrow-Hassidim-Lloyd (HHL) solver. A modular orchestration layer ensures data interoperability and coherence between quantum and classical components. This integration improves computational efficiency in high-dimensional, nonlinear problems and opens a path toward domain-specific quantum advantage in energy systems engineering. The results demonstrate the feasibility of incorporating quantum computing into classical modeling pipelines and suggest promising directions for future research in adaptive, scalable, and high-fidelity simulation environments. © 2025, Toronto Metropolitan University. All rights reserved.