Quantum machine learning approach for classification: case studies and implications

Nadia Ahmed Sharna; Emamul Islam

doi:10.1117/12.3010006

13 March 2024 Quantum machine learning approach for classification: case studies and implications

Nadia Ahmed Sharna, Emamul Islam

Proceedings Volume 12911, Quantum Computing, Communication, and Simulation IV; 129110I (2024) https://doi.org/10.1117/12.3010006
Event: SPIE Quantum West, 2024, San Francisco, California, United States

Abstract

With the advent of quantum computing, which offers exponential computational speedup compared to classical computers, and the constantly expanding field of machine learning, which focuses on extracting patterns and insights from data. The paper comprises two comprehensive case studies: Network Traffic Analysis and Earthquake Magnitude Classification. We were able to perform an overview of previous studies in this field and acknowledge the research gap while building a Quantum Machine Learning model that provides accuracy over 60% while using 4 Qubits and keeping the loss around 20%.

Conference Presentation

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

Citation Download Citation

Nadia Ahmed Sharna and Emamul Islam "Quantum machine learning approach for classification: case studies and implications", Proc. SPIE 12911, Quantum Computing, Communication, and Simulation IV, 129110I (13 March 2024); https://doi.org/10.1117/12.3010006

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