Current challenges of QML: a study on celestial object classification using real IBM quantum computers

James Chao; Ramiro Rodriguez

doi:10.1117/12.3011171

10 June 2024 Current challenges of QML: a study on celestial object classification using real IBM quantum computers

James Chao, Ramiro Rodriguez

Proceedings Volume 12993, Quantum Technologies 2024; 129930E (2024) https://doi.org/10.1117/12.3011171
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

This study provides insights into the current limitations of quantum machine learning compared to classical machine learning and identifies areas for future research. We present a novel approach that utilizes real IBM quantum computers to classify celestial objects within the extensive Sloan Digital Sky Survey Data Release 18 (SDSS-V DR18) dataset. Despite persistent challenges in both hardware and software, quantum computers are being explored as tools for enhancing machine learning performance in comparison to classical methods due to potential upside. This investigation delves into the untapped potential of quantum machine learning and quantum neural networks in tackling the complexities of processing vast telescope data. By leveraging quantum technologies, we aim to expedite the analysis of large complex data, unveiling hidden patterns, and propelling specialized fields such as astronomical research into the quantum era.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

James Chao and Ramiro Rodriguez "Current challenges of QML: a study on celestial object classification using real IBM quantum computers", Proc. SPIE 12993, Quantum Technologies 2024, 129930E (10 June 2024); https://doi.org/10.1117/12.3011171

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