Quantum neural network and its applications

Hyunsoo Lee, Gyu Seon Kim, Emily Jimin Roh +4 more

With the rapid advancement of quantum and artificial intelligence technologies, there is a growing trend to merge these two fields, a movement that is gaining momentum across both industry and academia. This emerging quantum artificial intelligence (QAI) is expected to achieve optimization and performance improvements in various mobility and network applications. The purpose of this article is to introduce quantum neural networks (QNNs) and explore applications of QAI in distributed network use cases. We begin by thoroughly investigating how QAI is being researched in the industry. Next, we discuss the fundamentals of quantum computing and QNNs. Following that, we examine QNN-based neural learning models for network and mobility applications, including quantum reinforcement learning, quantum convolutional neural networks, and quantum distributed learning. Finally, we consider the implications that QAI may have for future network and mobility applications.

Yuxuan Du, Xinbiao Wang, Naixu Guo +6 more

Fei Li, Shengmei Zhao, Baoyu Zheng

This paper describes a new kind of neural network - quantum neural network (QNN) and its application to recognition of continuous digits. QNN combines the advantages of neural modeling and fuzzy theoretic principles. Experiment results show that more than 15% error reduction is achieved on a speaker-independent continuous digits recognition task compared with the backpropagation (BP) network.

Davide Pastorello

Davide Pastorello

M. Vasuki, A. Karunamurthy, R. Ramakrishnan +1 more

In recent years, quantum computing has emerged as a potentially gamechanging technology, with applications across various disciplines, including AI and machine learning.In recent years, the combination of quantum computing and neural networks has led to the development of quantum neural networks (QNNs).This paper explores the potential of QNNs and their applications in solving complex problems that are challenging for classical neural networks.This paper explores the fundamental principles of quantum computing, the architecture of QNNs, and their advantages over classical neural networks.Furthermore, this will highlight key research areas and challenges in the development and utilization of QNNs.Through an in-depth analysis, it demonstrates the QNNs hold significant promise for addressing complex computational problems and advancing the field of artificial intelligence.