Paper Title :A Comparative Study of Image Classification with Quantum Neural Networks

Author :Nadine Matondo-Mvula, Hepseeba Kode, Laiali Almazaydeh, Khaled Elleithy

Article Citation :Nadine Matondo-Mvula ,Hepseeba Kode ,Laiali Almazaydeh ,Khaled Elleithy , (2022 ) " A Comparative Study of Image Classification with Quantum Neural Networks " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 28-36, Volume-10,Issue-12

Abstract : Abstract - This paper addresses aspects related to quantum machine-based classification such as data encoding into quantum states, Barren plateau, robustness, and effectiveness with developing a classical artificial neural versus fully and hybrid quantum neural network approach. The two approaches were compared with a classical artificial neural network using deep learning approaches concerning information processing speed, stability, and reliability. Moreover, fully and hybrid quantum approaches were tested on quantum simulators, considering the current limitations of quantum hardware. The models were trained on an image dataset describing the diagnosis of acute lymphoblastic leukemia. The test accuracy reached 98% for a classical artificial neural network approach, 99% for the hybrid quantum approach, and 64% for the fully quantum approach. Keywords - Quantum Computing, Quantum Machine Noisy Intermediate-Scale Quantum, Parameterized Quantum Circuits, Quantum Neural Networks, Variational Quantum Classifier.

Type : Research paper

Published : Volume-10,Issue-12

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