Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting hydrogen storage capacities for nanoporous materials, with a focus on metal–organic frameworks (MOFs). Utilizing a comprehensive dataset of 98,694 entries, we explore the efficacy of both methods in handling complex, nonlinear relationships. SVR utilizes polynomial kernels, but QSVR incorporates quantum feature maps and quantum kernels to improve regression in high‐dimensional environments. Our analysis, based on seven structural and physical properties of MOFs, reveals that SVR outperforms QSVR, achieving an average root mean squared error (RMSE) of 1.84 compared to QSVR's 2.11 across 100 experiments. Interestingly, when the feature set is reduced to two key parameters (density and void fraction), both models show improved performance, with QSVR demonstrating competitive accuracy (RMSE of 1.30) relative to SVR (RMSE of 1.10). These findings illuminate both the current limitations and the potential of QSVR in real‐world applications, particularly in materials informatics. While classical methods currently maintain an edge in accuracy, the promising performance of QSVR with reduced feature sets suggests significant potential for quantum machine learning approaches. As quantum hardware and algorithms continue to advance, we anticipate enhanced capabilities of QSVR in modeling complex, high‐dimensional datasets, potentially revolutionizing predictive modeling in materials science.