Quantum-Enhanced Support Vector Machine for Large-Scale Multi-class Stellar Classification

In this study, we introduce an innovative Quantum-enhanced Support Vector Machine (QSVM) approach for stellar classification problems. The QSVM algorithm in our work shows better performance than traditional methods such as K-Nearest Neighbors and Logistic Regression, particularly in handling complex binary and multi-class scenarios within the Harvard stellar classification system. The integration of quantum principles notably enhances classification accuracy, while GPU acceleration using the cuQuantum SDK ensures computational efficiency and scalability for large datasets in quantum simulators. This synergy not only accelerates the processing but also improves the accuracy of classifying diverse stellar types, setting a new benchmark in astronomical data analysis. Our findings underscore the transformative potential of quantum machine learning in astronomical research, marking a significant leap forward in both precision and processing speed for stellar classification. This advancement has broader implications for astrophysical and related scientific fields.