Quantum Neural Networks: Concepts, Applications, and Challenges

By Y. Kwak et al

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Table of Contents

I. Introduction
II. Quantum Deep Learning
A. Variational Quantum Circuits (VQC)
B. Quantum Neural Networks
III. Future Work Directions and Challenges
A. Applications of Quantum Deep Learning to Reinforcement Learning
B. Applications of Quantum Deep Learning to Communication Networks
C. Challenges
IV. Conclusion

Summary

Quantum deep learning is a research field that explores the use of quantum computing techniques for training deep neural networks. Quantum circuits have been found to mimic artificial neural networks, leading to the rapid growth of quantum deep learning. This paper explains the background, principles, major achievements, and challenges of quantum deep learning. It also discusses future research directions and application fields. Variational quantum circuits use rotation operator gates for tasks like optimization and classification. Quantum neural networks process data by encoding it into qubit states and applying rotation gates. Challenges include gradient vanishing, near-term device compatibility, and justifying quantum advantage over classical models. Applications of quantum deep learning include reinforcement learning and communication networks.