# Lean classical-quantum hybrid neural network model for image classification

arXiv: https://arxiv.org/abs/2412.02059 · HTML: https://mlsanity.com/paper/2412.02059/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Noise-Induced Barren Plateaus in Variational Quantum Algorithms](https://mlsanity.com/paper/2007.14384/) (2020)
- [Quantum advantage in learning from experiments](https://mlsanity.com/paper/2112.00778/) (2021)
- [Quantum machine learning for image classification](https://mlsanity.com/paper/2304.09224/) (2023)
- [Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines](https://mlsanity.com/paper/2207.05865/) (2022)
- [Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression](https://mlsanity.com/paper/2206.04804/) (2022)
- [Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits](https://mlsanity.com/paper/2111.07952/) (2021)

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

- [Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices](https://mlsanity.com/paper/2605.22097/) (2026)
- [Layer-wise QUBO-Based Training of CNN Classifiers for Quantum Annealing](https://mlsanity.com/paper/2603.02958/) (2026)
- [Tailor Made Embeddings for Quantum Machine Learning](https://mlsanity.com/paper/2606.26312/) (2026)
- [QuantumBoostNet: Hybrid Classical-Quantum Cardiac View Identification](https://mlsanity.com/paper/2608.27302/) (2026)

## Abstract (arXiv)

The integration of algorithms from quantum information with neural networks has enabled unprecedented advancements in various domains. Nonetheless, the application of quantum machine learning algorithms for image classification predominantly relies on traditional architectures such as variational quantum circuits. The performance of these models is closely tied to the scale of their parameters, with the substantial demand for parameters potentially leading to limitations in computational resources and a significant increase in computation time. In this paper, we introduce a Lean Classical-Quantum Hybrid Neural Network (LCQHNN), which achieves efficient classification performance with only four layers of variational circuits, thereby substantially reducing computational costs. Our experiments demonstrate that LCQHNN achieves 100%, 99.02%, and 85.55% classification accuracy on MNIST, FashionMNIST, and CIFAR-10 datasets. Under the same parameter conditions, the convergence speed of this method is also faster than that of traditional models. Furthermore, through visualization studies, it is found that the model effectively captures key data features during training and establishes a clear association between these features and their corresponding categories. This study confirms that the employment of quantum algorithms enhances the model's ability to handle complex classification problems.