# Variational Quantum Neural Networks (VQNNS) in Image Classification

arXiv: https://arxiv.org/abs/2303.05860 · HTML: https://mlsanity.com/paper/2303.05860/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Continuous-variable quantum neural networks](https://mlsanity.com/paper/1806.06871/) (2018)
- [On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification](https://mlsanity.com/paper/2109.09484/) (2021)
- [Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems](https://mlsanity.com/paper/1911.11071/) (2019)
- [A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification](https://mlsanity.com/paper/2201.01820/) (2022)
- [Quantum Optimization for Training Quantum Neural Networks](https://mlsanity.com/paper/2103.17047/) (2021)

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

- [Quantum machine learning for image classification](https://mlsanity.com/paper/2304.09224/) (2023)
- [A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks](https://mlsanity.com/paper/2504.12806/) (2025)
- [A Quantum Variational Approach to Prototypical Recurrent Unit](https://mlsanity.com/paper/2609.04354/) (2026)
- [Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment](https://mlsanity.com/paper/2609.28491/) (2026)

## Abstract (arXiv)

Quantum machine learning has established as an interdisciplinary field to overcome limitations of classical machine learning and neural networks. This is a field of research which can prove that quantum computers are able to solve problems with complex correlations between inputs that can be hard for classical computers. This suggests that learning models made on quantum computers may be more powerful for applications, potentially faster computation and better generalization on less data. The objective of this paper is to investigate how training of quantum neural network (QNNs) can be done using quantum optimization algorithms for improving the performance and time complexity of QNNs. A classical neural network can be partially quantized to create a hybrid quantum-classical neural network which is used mainly in classification and image recognition. In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs). We encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the network parameters. The parameters are tuned with an iterative quantum approximate optimisation (QAOA) mixer and problem hamiltonians. VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets (more complex) which converges the computation in lesser time than QNN with decent training accuracy.