In this study, we introduce an innovative Quantum-enhanced Support Vector Machine (QSVM) approach for stellar classification, leveraging the power of quantum computing and GPU acceleration. Our QSVM algorithm significantly surpasses traditional methods such as K-Nearest Neighbors (KNN) and Logistic Regression (LR), 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 process 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

翻译：本研究提出了一种创新的量子增强支持向量机（QSVM）恒星分类方法，融合了量子计算与GPU加速技术。我们的QSVM算法显著超越了K近邻（KNN）和逻辑回归（LR）等传统方法，尤其在处理哈佛恒星分类体系中的复杂二分类与多分类场景时表现突出。量子原理的整合显著提升了分类精度，而基于cuQuantum SDK的GPU加速则确保了量子模拟器处理大规模数据集时的计算效率与可扩展性。这种协同作用不仅加速了处理流程，还提升了多样恒星类型分类的准确性，为天文数据分析树立了新标杆。研究结果表明，量子机器学习在天文研究中具有变革性潜力，标志着恒星分类在精度与处理速度上的重大突破，并为天体物理学及相关科学领域带来更深远的影响。