# Random Quantum Neural Networks (RQNN) for Noisy Image Recognition

arXiv: https://arxiv.org/abs/2203.01764 · HTML: https://mlsanity.com/paper/2203.01764/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Fast and energy-efficient neuromorphic deep learning with first-spike times](https://mlsanity.com/paper/1912.11443/) (2019)
- [An end-to-end trainable hybrid classical-quantum classifier](https://mlsanity.com/paper/2102.02416/) (2021)
- [Robust High-dimensional Memory-augmented Neural Networks](https://mlsanity.com/paper/2010.01939/) (2020)
- [Synaptic metaplasticity in binarized neural networks](https://mlsanity.com/paper/2101.07592/) (2021)
- [A hybrid quantum-classical neural network with deep residual learning](https://mlsanity.com/paper/2012.07772/) (2020)
- [Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation](https://mlsanity.com/paper/2009.06767/) (2020)
- [An Artificial Spiking Quantum Neuron](https://mlsanity.com/paper/1907.06269/) (2019)
- [Quantum Superposition Inspired Spiking Neural Network](https://mlsanity.com/paper/2010.12197/) (2020)

## Built on by (most cited first, 4 of 4)

- [Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks](https://mlsanity.com/paper/2310.01515/) (2023)
- [Noise robust neural network architecture](https://mlsanity.com/paper/2305.09276/) (2023)
- [Estimating the Effect of Crosstalk Error on Circuit Fidelity Using Noisy Intermediate-Scale Quantum Devices](https://mlsanity.com/paper/2402.06952/) (2024)
- [FRQI Pairs method for image classification using Quantum Recurrent Neural Network](https://mlsanity.com/paper/2512.11499/) (2025)

## Abstract (arXiv)

Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision making, signal processing, and image recognition tasks. However, their implementation has been limited to deterministic digital systems that output probability distributions in lieu of stochastic behaviors of random spiking signals. We introduce the novel class of supervised Random Quantum Neural Networks (RQNNs) with a robust training strategy to better exploit the random nature of the spiking RNN. The proposed RQNN employs hybrid classical-quantum algorithms with superposition state and amplitude encoding features, inspired by quantum information theory and the brain's spatial-temporal stochastic spiking property of neuron information encoding. We have extensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via the PennyLane Quantum simulator with a limited number of qubits. Experiments on the MNIST, FashionMNIST, and KMNIST datasets demonstrate that the proposed RQNN model achieves an average classification accuracy of 94.9%. Additionally, the experimental findings illustrate the proposed RQNN's effectiveness and resilience in noisy settings, with enhanced image classification accuracy when compared to the classical counterparts (RNNs), classical Spiking Neural Networks (SNNs), and the classical convolutional neural network (AlexNet). Furthermore, the RQNN can deal with noise, which is useful for various applications, including computer vision in NISQ devices. The PyTorch code (https://github.com/darthsimpus/RQN) is made available on GitHub to reproduce the results reported in this manuscript.