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

TL;DR

This paper presents a quantum-enhanced support vector machine that leverages quantum computing and GPU acceleration to improve the accuracy and efficiency of large-scale stellar classification, outperforming traditional methods.

Contribution

It introduces a novel QSVM algorithm that combines quantum principles with GPU acceleration, achieving superior classification accuracy and scalability for astronomical datasets.

Findings

QSVM outperforms traditional classifiers like KNN and Logistic Regression

GPU acceleration significantly reduces computation time

Enhanced accuracy in classifying diverse stellar types

Abstract

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