Quantum classification for synthetic aperture radar

• Main Authors: Naik, Salil, Vaughn, Nolan, Uehara, Glen, Spanias, Andreas, Jaskie, Kristen
• Format: Conference Proceeding
• Language: English
• Published: SPIE 07.06.2024
• ISBN: 1510673962; 9781510673960
• ISSN: 0277-786X
• DOI: 10.1117/12.3016462

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.