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

我们引入了一种革新性的量子增强支持向量机（QSVM）方法，利用量子计算和 GPU 加速来进行恒星分类，QSVM 算法在处理复杂的二元和多类别情况下显著优于传统方法，尤其是在哈佛恒星分类系统中。量子原理的整合显著提高了分类准确性，而使用 cuQuantum SDK 进行 GPU 加速确保了量子模拟器中大数据集的计算效率和可扩展性。这种协同作用不仅加快了处理过程，还提高了分类不同恒星类型的准确性，为天体物理学和相关科学领域的研究提供了量子机器学习的变革潜力，为恒星分类的精度和处理速度跃升到一个新的水平。这一进展对天体物理学和相关科学领域具有广泛的影响。