Quantum Neural Networks: Concepts, Applications, and Challenges
arXiv:2108.01468
Abstract
Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks. The research topics and directions of deep learning and quantum computing have been separated for long time, however by discovering that quantum circuits can act like artificial neural networks, quantum deep learning research is widely adopted. This paper explains the backgrounds and basic principles of quantum deep learning and also introduces major achievements. After that, this paper discusses the challenges of quantum deep learning research in multiple perspectives. Lastly, this paper presents various future research directions and application fields of quantum deep learning.
References in corpus (11)
- Quantum Computing in the NISQ era and beyond
- Playing Atari with Deep Reinforcement Learning
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Barren plateaus in quantum neural network training landscapes
- A Quantum Approximate Optimization Algorithm
- Quantum machine learning in feature Hilbert spaces
- Quantum Convolutional Neural Networks
- Quantum reinforcement learning
- Universal Variational Quantum Computation
- Wireless Video Caching and Dynamic Streaming under Differentiated Quality Requirements
- Advances in Quantum Deep Learning: An Overview