A quantum-enhanced support vector machine for galaxy classification

TL;DR

This paper introduces a quantum-enhanced support vector machine for galaxy classification, demonstrating comparable performance to classical methods and exploring the potential for quantum advantage in astronomical data analysis.

Contribution

It presents a novel quantum SVM algorithm for galaxy morphology classification and evaluates its performance against classical algorithms using simulated and real quantum devices.

Findings

Quantum SVM achieves similar ROC AUC to classical SVM on galaxy data.

Quantum device performance aligns with simulations for small datasets.

A necessary condition for quantum advantage in this context is discussed.

Abstract

Galaxy morphology, a key tracer of the evolution of a galaxy's physical structure, has motivated extensive research on machine learning techniques for efficient and accurate galaxy classification. The emergence of quantum computers has generated optimism about the potential for significantly improving the accuracy of such classifications by leveraging the large dimensionality of quantum Hilbert space. This paper presents a quantum-enhanced support vector machine algorithm for classifying galaxies based on their morphology. The algorithm requires the computation of a kernel matrix, a task that is performed on a simulated quantum computer using a quantum circuit conjectured to be intractable on classical computers. The result shows similar performance between classical and quantum-enhanced support vector machine algorithms. For a training size of $40$k, the receiver operating characteristic curve for differentiating ellipticals and spirals has an under-curve area (ROC AUC) of $0.946\pm 0.005$ for both classical and quantum-enhanced algorithms. Additionally, we demonstrate for a small dataset that the performance of a noise-mitigated quantum SVM algorithm on a quantum device is in agreement with simulation. Finally, a necessary condition for achieving a potential quantum advantage is presented. This investigation is among the very first applications of quantum machine learning in astronomy and highlights their potential for further application in this field.