# Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)

arXiv: https://arxiv.org/abs/2109.02862 · HTML: https://mlsanity.com/paper/2109.02862/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [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)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Drug Discovery Approaches using Quantum Machine Learning](https://mlsanity.com/paper/2104.00746/) (2021)
- [Quantum Enhanced Filter: QFilter](https://mlsanity.com/paper/2104.03418/) (2021)

## Built on by (most cited first, 14 of 14)

- [Quantum machine learning for image classification](https://mlsanity.com/paper/2304.09224/) (2023)
- [Differentiable Quantum Architecture Search for Quantum Reinforcement Learning](https://mlsanity.com/paper/2309.10392/) (2023)
- [Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks](https://mlsanity.com/paper/2402.11687/) (2024)
- [Quantum Visual Fields with Neural Amplitude Encoding](https://mlsanity.com/paper/2508.10900/) (2025)
- [Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses](https://mlsanity.com/paper/2204.03625/) (2022)
- [3D-QAE: Fully Quantum Auto-Encoding of 3D Point Clouds](https://mlsanity.com/paper/2311.05604/) (2023)
- [A Hybrid Quantum Neural Network for Split Learning](https://mlsanity.com/paper/2409.16593/) (2024)
- [STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud](https://mlsanity.com/paper/2405.18746/) (2024)
- [Quantum Deep Equilibrium Models](https://mlsanity.com/paper/2410.23940/) (2024)
- [Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era](https://mlsanity.com/paper/2411.14412/) (2024)
- [DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms](https://mlsanity.com/paper/2307.12449/) (2023)
- [Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction](https://mlsanity.com/paper/2505.10037/) (2025)
- [Hybrid Quantum-Classical Selective State Space Artificial Intelligence](https://mlsanity.com/paper/2511.08349/) (2025)
- [Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images](https://mlsanity.com/paper/2603.28995/) (2026)

## Abstract (arXiv)

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. On one hand, QML models can be useful in both of these tasks. Convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoencoder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.