# Variational Quanvolutional Neural Networks with enhanced image encoding

arXiv: https://arxiv.org/abs/2106.07327 · HTML: https://mlsanity.com/paper/2106.07327/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [An end-to-end trainable hybrid classical-quantum classifier](https://mlsanity.com/paper/2102.02416/) (2021)
- [Quantum support vector machine for big data classification](https://mlsanity.com/paper/1307.0471/) (2013)
- [Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images](https://mlsanity.com/paper/2102.06535/) (2021)
- [Variational Quantum Circuit Model for Knowledge Graphs Embedding](https://mlsanity.com/paper/1903.00556/) (2019)

## Built on by (most cited first, 13 of 13)

- [Variational Quantum Reinforcement Learning via Evolutionary Optimization](https://mlsanity.com/paper/2109.00540/) (2021)
- [Quantum Architecture Search via Continual Reinforcement Learning](https://mlsanity.com/paper/2112.05779/) (2021)
- [Quantum-classical convolutional neural networks in radiological image classification](https://mlsanity.com/paper/2204.12390/) (2022)
- [Reservoir Computing via Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2211.02612/) (2022)
- [ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks](https://mlsanity.com/paper/2402.09146/) (2024)
- [Comparing Quantum Encoding Techniques](https://mlsanity.com/paper/2410.09121/) (2024)
- [Predict better with less training data using a QNN](https://mlsanity.com/paper/2206.03960/) (2022)
- [Formal Verification of Variational Quantum Circuits](https://mlsanity.com/paper/2507.10635/) (2025)
- [QDCNN: Quantum Dilated Convolutional Neural Network](https://mlsanity.com/paper/2110.15667/) (2021)
- [Quantum-AI empowered Intelligent Surveillance: Advancing Public Safety Through Innovative Contraband Detection](https://mlsanity.com/paper/2309.03231/) (2023)
- [Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks](https://mlsanity.com/paper/2410.05777/) (2024)
- [Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization](https://mlsanity.com/paper/2512.14181/) (2025)
- [Pixel-Translation-Equivariant Quantum Convolutional Neural Networks via Fourier Multiplexers](https://mlsanity.com/paper/2604.06094/) (2026)

## Abstract (arXiv)

Image classification is an important task in various machine learning applications. In recent years, a number of classification methods based on quantum machine learning and different quantum image encoding techniques have been proposed. In this paper, we study the effect of three different quantum image encoding approaches on the performance of a convolution-inspired hybrid quantum-classical image classification algorithm called quanvolutional neural network (QNN). We furthermore examine the effect of variational - i.e. trainable - quantum circuits on the classification results. Our experiments indicate that some image encodings are better suited for variational circuits. However, our experiments show as well that there is not one best image encoding, but that the choice of the encoding depends on the specific constraints of the application.