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

The data consists of 100,000 observations of space taken by the SDSS (Sloan Digital Sky Survey). Every data point is described by 17 feature columns and 1 class column which identifies it to be either a star, galaxy, or quasar [1]. Note: The SDSS data is under the public domain. Please refer to the citation at the end. Celestial sphere: The celestial sphere is an imaginary sphere that has a large radius and is concentric on Earth.

Twinkle twinkle little star, how I wonder what you are… This free evening talk will explore Machine Learning applications in astrophysics, using manual star classification techniques as an example. In my previous life, I was an astronomer and one of the big tasks many PhD students face is manual star classification. But why ask a student to do what a machine should be able to do too? In this talk, I use the spectra (stellar flux vs wavelength) of identified stars to build a classifier which will detect the identity of the stars. Using a very simple non linear SVM, I achieve an 86% accuracy with my model.