2025/02/10 by Shinyoung Kang, Kang, Shinyoung, Younghun Kim +3 · 6 citations
Computer Science · Materials Science · #Quantum Computing Algorithms and Architecture #Quantum-Dot Cellular Automata #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2502.06339
Multivariate (MTV) porous materials exhibit unique structural complexities based on diverse spatial arrangements of multiple building block combinations. These materials possess potential synergistic functionalities that exceed the sum of their individual components. However, the exponentially increasing design complexity of these materials poses challenges for accurate ground-state configuration prediction and design. To address this, a Hamiltonian model was developed for quantum computing that integrates compositional, structural, and balance constraints, enabling efficient optimization of the MTV configurations. The model employs a graph-based representation to encode linkers as qubits. To validate our model, a variational quantum circuit was constructed and executed using the Sampling VQE algorithm. Simulations on experimentally known MTV porous materials successfully reproduced their ground-state configurations, demonstrating the validity of our model. Furthermore, VQE calculations were performed on real quantum hardware for validation purposes, signaling a first step toward a practical quantum algorithm for the rational design of porous materials.