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。

量子增强支持向量机与 GPU 加速的大规模恒星分类

Quantum-Enhanced Support Vector Machine for Large-Scale Stellar  Classification with GPU Acceleration

Galah is an ongoing high-resolution spectroscopic survey with the goal of
disentangling the formation history of the Milky Way, using the fossil remnants
of disrupted star formation sites which are now dispersed around the Galaxy. It
is targeting a randomly selected, magnitude limited ($V \leq 14$) sample of
stars, with the goal of observing one million objects. To date, 300,000 spectra
have been obtained. Not all of them are correctly processed by parameter
estimation pipelines and we need to know about them. We present a
semi-automated classification scheme which identifies different types of
peculiar spectral morphologies, in an effort to discover and flag potentially
problematic spectra and thus help to preserve the integrity of the survey's
results. To this end we employ a recently developed dimensionality reduction
technique t-SNE (t-distributed Stochastic Neighbour Embedding), which enables
us to represent the complex spectral morphology in a two-dimensional projection
map while still preserving the properties of the local neighbourhoods of
spectra. We find that the majority (178,483) of the 209,533 Galah spectra
considered in this study represents normal single stars, whereas 31,050
peculiar and problematic spectra with very diverse spectral features pertaining
to 28,579 stars are distributed into 10 classification categories: Hot stars,
Cool metal-poor giants, Molecular absorption bands, Binary stars,
H$\alpha$/H$\beta$ emission, H$\alpha$/H$\beta$ emission superimposed on
absorption, H$\alpha$/H$\beta$ P-Cygni, H$\alpha$/H$\beta$ inverted P-Cygni,
Lithium absorption, and Problematic. Classified spectra with supplementary
information are presented in the catalogue, indicating candidates for follow-up
observations and population studies of the short-lived phases of stellar
evolution.

Galah 是一项使用高分辨率光谱观测汇散在银河系中的星形成遗迹，解恋银河系形成历史的计划，已经获得了 30 万颗星的光谱数据，但需要一种半自动分类方案来识别星的特征，这里应用 t-SNE 这种降维算法，将 209,533 个星的光谱分为十种分类，并列出分类目录方便后续研究。