Quantum classification for synthetic aperture radar

Salil Naik; Nolan Vaughn; Glen Uehara; Andreas Spanias; Kristen Jaskie

doi:10.1117/12.3016462

7 June 2024 Quantum classification for synthetic aperture radar

Salil Naik, Nolan Vaughn, Glen Uehara, Andreas Spanias, Kristen Jaskie

Proceedings Volume 13039, Automatic Target Recognition XXXIV; 130390H (2024) https://doi.org/10.1117/12.3016462
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The field of quantum computing, especially quantum machine learning (QML), has been the subject of much research in recent years. Leveraging the quantum properties of superposition and entanglement promises exponential decrease in computation costs. With the promises of increased speed and accuracy in the quantum paradigm, many classical machine learning algorithms have been adapted to run on quantum computers, typically using a quantum-classical hybrid model. While some work has been done to compare classical and quantum classification algorithms in the Electro-Optical (EO) image domain, this paper will compare the performance of classical and quantum-hybrid classification algorithms in their applications on Synthetic Aperture Radar (SAR) data using the MSTAR dataset. We find that there is no significant difference in classification performance when training with quantum algorithms in ideal simulators as compared to their classical counterparts. However, the true performance benefits will become more apparent as the hardware matures.

Conference Presentation

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

Citation Download Citation

Salil Naik, Nolan Vaughn, Glen Uehara, Andreas Spanias, and Kristen Jaskie "Quantum classification for synthetic aperture radar", Proc. SPIE 13039, Automatic Target Recognition XXXIV, 130390H (7 June 2024); https://doi.org/10.1117/12.3016462

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