Journal Article

An Evaluation of Hardware-Efficient Quantum Neural Networks for Image Data Classification

Incheon Paik, Truong Cong Thang, Tuyen Nguyen, Yutaka Watanobe

2022

Overview

Quantum computing is expected to fundamentally change computer systems in the future. Recently, a new research topic of quantum computing is the hybrid quantum–classical approach for machine learning, in which a parameterized quantum circuit, also called quantum neural network (QNN), is optimized by a classical computer. This hybrid approach can have the benefits of both quantum computing and classical machine learning methods. In this early stage, it is of crucial importance to understand the new characteristics of quantum neural networks for different machine learning tasks. In this paper, we will study quantum neural networks for the task of classifying images, which are high-dimensional spatial data. In contrast to previous evaluations of low-dimensional or scalar data, we will investigate the impacts of practical encoding types, circuit depth, bias term, and readout on classification performance on the popular MNIST image dataset. Various interesting findings on learning behaviors of different QNNs are obtained through experimental results. To the best of our knowledge, this is the first work that considers various QNN aspects for image data.

• Publisher: MDPI AG
• Subject: Artificial intelligence / Bias / Circuits / Classification / Cognitive tasks / Computers / Datasets / Image classification / Image contrast / Machine learning / Medical imaging / Neural networks / Quantum computing / quantum machine learning; parameterized quantum circuit; quantum neural network; image classification / Simulation / Spatial data