# QDNN: DNN with Quantum Neural Network Layers

arXiv: https://arxiv.org/abs/1912.12660 · HTML: https://mlsanity.com/paper/1912.12660/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus](https://mlsanity.com/paper/2102.01828/) (2021)
- [Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes](https://mlsanity.com/paper/2304.11247/) (2023)
- [Hybrid quantum ResNet for car classification and its hyperparameter optimization](https://mlsanity.com/paper/2205.04878/) (2022)
- [Forecasting steam mass flow in power plants using the parallel hybrid network](https://mlsanity.com/paper/2307.09483/) (2023)
- [QFCNN: Quantum Fourier Convolutional Neural Network](https://mlsanity.com/paper/2106.10421/) (2021)
- [Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems](https://mlsanity.com/paper/2304.10159/) (2023)

## Abstract (arXiv)

In this paper, we introduce a quantum extension of classical DNN, QDNN. The QDNN consisting of quantum structured layers can uniformly approximate any continuous function and has more representation power than the classical DNN. It still keeps the advantages of the classical DNN such as the non-linear activation, the multi-layer structure, and the efficient backpropagation training algorithm. Moreover, the QDNN can be used on near-term noisy intermediate-scale quantum processors. A numerical experiment for image classification based on quantum DNN is given, where a high accuracy rate is achieved.