# Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data

arXiv: https://arxiv.org/abs/2307.11792 · HTML: https://mlsanity.com/paper/2307.11792/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Absence of Barren Plateaus in Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2011.02966/) (2020)
- [A divide-and-conquer algorithm for quantum state preparation](https://mlsanity.com/paper/2008.01511/) (2020)
- [Quantum machine learning with subspace states](https://mlsanity.com/paper/2202.00054/) (2022)
- [Quantum Embedding Search for Quantum Machine Learning](https://mlsanity.com/paper/2105.11853/) (2021)
- [Synthetic weather radar using hybrid quantum-classical machine learning](https://mlsanity.com/paper/2111.15605/) (2021)
- [Biomarker Discovery with Quantum Neural Networks: A Case-study in CTLA4-Activation Pathways](https://mlsanity.com/paper/2306.01745/) (2023)

## Built on by (most cited first, 3 of 3)

- [Exploring Quantum Machine Learning for Weather Forecasting](https://mlsanity.com/paper/2509.01422/) (2025)
- [Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification](https://mlsanity.com/paper/2507.02086/) (2025)
- [Leveraging Quantum-Based Architectures for Robust Diagnostics](https://mlsanity.com/paper/2511.12386/) (2025)

## Abstract (arXiv)

Quantum Machine Learning (QML) has come into the limelight due to the exceptional computational abilities of quantum computers. With the promises of near error-free quantum computers in the not-so-distant future, it is important that the effect of multi-qubit interactions on quantum neural networks is studied extensively. This paper introduces a Quantum Convolutional Network with novel Interaction layers exploiting three-qubit interactions, while studying the network's expressibility and entangling capability, for classifying both image and one-dimensional data. The proposed approach is tested on three publicly available datasets namely MNIST, Fashion MNIST, and Iris datasets, flexible in performing binary and multiclass classifications, and is found to supersede the performance of existing state-of-the-art methods.