Quantum Computing Based Design of Multivariate Porous Materials

• URL: http://arxiv.org/abs/2502.06339v1
• Date: Mon, 10 Feb 2025 10:40:22 GMT
• Title: Quantum Computing Based Design of Multivariate Porous Materials
• Authors: Shinyoung Kang, Younghun Kim, Jihan Kim,
• Abstract summary: We propose a Hamiltonian model for quantum computing that integrates compositional, structural, and balance constraints directly into the Hamiltonian.<n>Our framework leads to exponentially efficient exploration of a vast search space of the linkers to identify optimal configurations.<n> Simulations on experimentally known MTV porous materials successfully reproduced their ground-state configurations.
• Score: 1.0923877073891446
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multivariate (MTV) porous materials exhibit unique structural complexities based on their 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 significant challenges for accurate ground-state configuration prediction and design. To address this, we propose a Hamiltonian model for quantum computing that integrates compositional, structural, and balance constraints directly into the Hamiltonian, enabling efficient optimization of the MTV configurations. The model employs a graph-based representation to encode linker types as qubits. Our framework leads to exponentially efficient exploration of a vast search space of the linkers to identify optimal configurations based on predefined design variables. To validate our model, a variational quantum circuit was constructed and executed using the Sampling VQE algorithm in the IBM Qiskit. Simulations on experimentally known MTV porous materials (e.g. Cu-THQ-HHTP, Py-MVDBA-COF, MUF-7, and SIOC-COF2) successfully reproduced their ground-state configurations, demonstrating the validity of our model.

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