New directions in quantum neural networks research

Sanjay Chakraborty

The working environment in healthcare analytics is transforming with the emergence of healthcare 5.0 and the advancements in quantum neural networks. In addition to analyzing a comprehensive set of case studies, we also review relevant literature from the fields of quantum computing applications and smart healthcare analytics, focusing on the implications of quantum deep neural networks. This study aims to shed light on the existing research gaps regarding the implications of quantum neural networks in healthcare analytics. We argue that the healthcare industry is currently transitioning from automation towards genuine collaboration with quantum networks, which presents new avenues for research and exploration. Specifically, this study focuses on evaluating the performance of Healthcare 5.0, which involves the integration of diverse quantum machine learning and quantum neural network systems. This study also explores a range of potential challenges and future directions for Healthcare 5.0, particularly focusing on the integration of quantum neural networks.

Nasrullah Masud, Abdus Sobhan, Md Tanvir Islam Sourav +1 more

V. S. Moskvin

This paper explores the application of Quantum Neural Networks (QNNs) in the field of traffic prediction, an integral aspect of modern transportation management. Traditional machine learning methods like neural networks, decision trees, and support vector machines have been commonly used for traffic prediction. However, the advent of quantum computing offers promising new directions. QNNs, embodying principles of quantum mechanics, have the potential to handle large, complex traffic datasets with greater efficiency and possibly provide more accurate predictions. Despite being in the early stages of research and development, the potential advantages of QNNs in terms of handling complexity, leveraging quantum superposition, entanglement, and interference make them a compelling area of study. Nevertheless, the practical implementation of QNNs for traffic prediction entails overcoming significant challenges related to quantum computing hardware and the development of new quantum machine learning algorithms and frameworks. Ultimately, QNNs could revolutionize traffic prediction and numerous other fields.

S. K. Jeswal, Snehashish Chakraverty

Yanxuan Lü, Qing Zhong Gao, Jinhu Lü +2 more

In recent years, artificial neural networks have been widely used in various fields and have shown strong computational intelligence. At the same time, quantum computing hardware has been developed rapidly. In particular, near term quantum processors have relatively stable computing capabilities and robustness against decoherence. With the aid of this powerful quantum technique, nowadays many commercial cloud quantum computing companies can provide online quantum computing experimental platforms for scholars around the world. Various technical developments have promoted the emergence of many new research ideas and experimental progress in the cross-field of quantum neural networks. In this paper, the research ideas of early quantum neural networks are summarized first. The data encoding, algorithm flows, and quantum circuit design of quantum neural networks based on recent quantum processors are then reviewed and analyzed. Finally, several key scientific problems in the field of quantum neural networks are summarized, and prospective future research directions are presented.

Mohammad R. Khosravi, Maryam Zekri