# Quantum Neural Networks: Concepts, Applications, and Challenges

arXiv: https://arxiv.org/abs/2108.01468 · HTML: https://mlsanity.com/paper/2108.01468/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [Recurrent Quantum Neural Networks](https://mlsanity.com/paper/2006.14619/) (2020)
- [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020)
- [Advances in Quantum Deep Learning: An Overview](https://mlsanity.com/paper/2005.04316/) (2020)
- [Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks](https://mlsanity.com/paper/2001.03611/) (2020)
- [Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks](https://mlsanity.com/paper/1902.10932/) (2019)
- [Wireless Video Caching and Dynamic Streaming under Differentiated Quality Requirements](https://mlsanity.com/paper/1806.01594/) (2018)
- [Dynamic Power Allocation and User Scheduling for Power-Efficient and Low-Latency Communications](https://mlsanity.com/paper/1807.00682/) (2018)
- [A Personalized Preference Learning Framework for Caching in Mobile Networks](https://mlsanity.com/paper/1904.06744/) (2019)
- [Joint Distributed Link Scheduling and Power Allocation for Content Delivery in Wireless Caching Networks](https://mlsanity.com/paper/1911.13010/) (2019)
- [Securing Heterogeneous IoT with Intelligent DDoS Attack Behavior Learning](https://mlsanity.com/paper/1711.06041/) (2017)

## Built on by (most cited first, 21 of 21)

- [A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead](https://mlsanity.com/paper/2310.10315/) (2023)
- [Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design](https://mlsanity.com/paper/2203.10443/) (2022)
- [Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation](https://mlsanity.com/paper/2108.06849/) (2021)
- [Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications](https://mlsanity.com/paper/2307.00908/) (2023)
- [3D-QAE: Fully Quantum Auto-Encoding of 3D Point Clouds](https://mlsanity.com/paper/2311.05604/) (2023)
- [Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification](https://mlsanity.com/paper/2210.16731/) (2022)
- [Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms](https://mlsanity.com/paper/2306.08060/) (2023)
- [QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks](https://mlsanity.com/paper/2604.07013/) (2026)
- [Quantum Deep Dreaming: A Novel Approach for Quantum Circuit Design](https://mlsanity.com/paper/2211.04343/) (2022)
- [Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation](https://mlsanity.com/paper/2311.13810/) (2023)
- [Hybrid quantum-classical convolutional neural network for phytoplankton classification](https://mlsanity.com/paper/2303.03707/) (2023)
- [Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability](https://mlsanity.com/paper/2402.11645/) (2024)
- [Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis](https://mlsanity.com/paper/2502.07806/) (2025)
- [On the explainability of quantum neural networks based on variational quantum circuits](https://mlsanity.com/paper/2301.05549/) (2023)
- [WavPool: A New Block for Deep Neural Networks](https://mlsanity.com/paper/2306.08734/) (2023)
- [Multi-Scale Feature Fusion Quantum Depthwise Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/2405.13515/) (2024)
- [A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables](https://mlsanity.com/paper/2503.07633/) (2025)
- [Trainable Quantum Neural Network for Multiclass Image Classification with the Power of Pre-trained Tree Tensor Networks](https://mlsanity.com/paper/2504.14995/) (2025)
- [Quantum-enhanced long short-term memory with attention for spatial permeability prediction in oilfield reservoirs](https://mlsanity.com/paper/2601.02818/) (2026)
- [Noise Models Impacts and Mitigation Strategies in Photonic Quantum Machine Learning](https://mlsanity.com/paper/2603.09645/) (2026)
- [QiT: Quantum-Inspired Transformer for Visual Recognition Task](https://mlsanity.com/paper/2609.17789/) (2026)

## Abstract (arXiv)

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.