Quantum-Enhanced Computational Catalysis: Optimizing Reaction Pathway Simulations with Advanced Tensor Decomposition Techniques

Abstract

Quantum-enhanced computational catalysis offers a transformative framework for modeling complex reaction pathways with unprecedented accuracy and scalability. As catalytic processes often involve strongly correlated electrons, multidimensional potential energy surfaces, and dynamic coupling between nuclear and electronic degrees of freedom, classical simulation methods face steep computational barriers. This study explores a hybrid quantum–classical workflow that integrates quantum algorithms for electronic structure with advanced tensor decomposition techniques to compress, analyze, and optimize high-dimensional reaction data. By leveraging tensor-network representations—such as matrix product states, tensor-train formats, and hierarchical Tucker decompositions—the approach systematically reduces computational cost while maintaining chemically relevant accuracy. The research demonstrates how tensor-optimized quantum simulations can accelerate transition-state discovery, enhance reaction pathway resolution, and improve predictions of catalytic efficiency across heterogeneous, homogeneous, and photocatalytic systems. The synergy between quantum solvers and tensor decomposition establishes a powerful platform for next-generation catalytic modeling and materials discovery.