Quantum Neural Networks and Quantum Intelligence

Soujanya Ray, Soujanya Ray

Yuxuan Du, Xinbiao Wang, Naixu Guo +6 more

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.

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.

Eleanor WHITMORE

A promising combination of artificial intelligence and quantum computing, quantum neural networks (QNNs) have the potential to completely transform the way neural networks interpret and learn from data. QNNs can outperform classical neural networks in terms of speed, efficiency, and scalability by utilizing the special qualities of quantum physics, such as superposition, entanglement, and quantum parallelism. The basic ideas of QNNs, their structures, and the ways in which quantum algorithms can be incorporated into machine learning processes to improve problem-solving skills are all examined in this study. current developments in QNNs, emphasizing how they can be used for challenging applications including pattern recognition, optimization, and high-dimensional data space decision-making. We also go over the difficulties in creating scalable QNN models and the possible future applications of QNNs in artificial intelligence. By utilizing the potential of quantum computing, QNNs have the potential to advance artificial intelligence by providing answers to issues that are now intractable through traditional methods.