# QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation

arXiv: https://arxiv.org/abs/2210.05443 · HTML: https://mlsanity.com/paper/2210.05443/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage](https://mlsanity.com/paper/2006.14815/) (2020)
- [Near-Term Advances in Quantum Natural Language Processing](https://mlsanity.com/paper/2206.02171/) (2022)
- [Quantum Computing -- A new scientific revolution in the making](https://mlsanity.com/paper/2106.11840/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity](https://mlsanity.com/paper/2402.15333/) (2024)
- [Towards Quantum Machine Learning for Malicious Code Analysis](https://mlsanity.com/paper/2508.19381/) (2025)

## Abstract (arXiv)

Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend and demonstrate an adaption of the Classical Convolutional Neural Networks to quantum systems - namely QuCNN. QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. With QuCNN, back propagation can be achieved through a single-ancilla qubit quantum routine. QuCNN is validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images, comparing the back propagated gradients, and training a filter state against an ideal target state.