Research Article
Neural Networks with Variational Quantum Circuits
@INPROCEEDINGS{10.1007/978-3-031-47359-3_15, author={Syed Muhammad Abuzar Rizvi and Muhammad Shohibul Ulum and Naema Asif and Hyundong Shin}, title={Neural Networks with Variational Quantum Circuits}, proceedings={Industrial Networks and Intelligent Systems. 9th EAI International Conference, INISCOM 2023, Ho Chi Minh City, Vietnam, August 2-3, 2023, Proceedings}, proceedings_a={INISCOM}, year={2023}, month={10}, keywords={Quantum Computing Variational Quantum Circuits Neural Networks Image Classification}, doi={10.1007/978-3-031-47359-3_15} }- Syed Muhammad Abuzar Rizvi
Muhammad Shohibul Ulum
Naema Asif
Hyundong Shin
Year: 2023
Neural Networks with Variational Quantum Circuits
INISCOM
Springer
DOI: 10.1007/978-3-031-47359-3_15
Abstract
The field of machine learning is an interdisciplinary area that aims to extract useful information from data through mathematical means. Integrating quantum computing with machine learning has led to exciting new avenues of research, where quantum mechanics principles are applied to enhance and optimize classical machine learning algorithms. In this study, we explore hybrid quantum-classical neural networks with an approach that combines both classical and quantum computing. We achieve this by implementing a variational quantum circuit as the output layer of a classical convolutional neural network. We use this hybrid neural network to classify images of digits from the MNIST dataset. Using this approach, we were able to classify images with high accuracy. Furthermore, due to its flexibility, this hybrid algorithm can be adapted to explore the potential of quantum computing especially in the era of noisy intermediate-scale quantum devices.
