Development of a computational framework for geometry optimization in quantum simulations

Abstract

Materials research plays a crucial role in various industries by linking material properties to performance and safety in real-world applications. In particular, aluminum-magnesium alloys used in aerospace engineering exemplify the challenges posed by hydrogen embrittlement, necessitating advanced methods for material characterization and optimization. Traditional experimental approaches to material testing are costly and time-intensive, motivating the adoption of computational simulations to analyze and predict material behavior at the atomic level. This thesis focuses on the development of a quantum simulation based algorithm to relax crystal structures and its application on case studies. Quantum algorithms like the Variational Quantum Eigensolver (VQE) offer advantages over classical approaches like Full Configuration Interaction (FCI) and Density Functional Theory (DFT) by efficiently managing large active spaces and accounting for electronic excitations beyond the ground-state. The developed algorithm demonstrates robust performance in geometry optimizations across molecular and crystal systems. Case studies on chromium(VI)-oxide and face-centered cubic (FCC) aluminum with H2 in the octahedral site highlight the algorithms capacity to model complex structural behaviors. In conclusion, the quantum simulation-based approach effectively relaxes crystal geometries, producing results closely aligned with established DFT methods. This work paves the way for enhanced quantum-assisted material optimization, with potential impacts on industrial applications requiring precise and efficient simulation tools