# Quantum Enhanced Filter: QFilter

arXiv: https://arxiv.org/abs/2104.03418 · HTML: https://mlsanity.com/paper/2104.03418/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 7 Apr 2021.

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

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Improving Generalization Performance by Switching from Adam to SGD](https://mlsanity.com/paper/1712.07628/) (2017)
- [quantum Case-Based Reasoning (qCBR)](https://mlsanity.com/paper/2104.00409/) (2021)

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

- [Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)](https://mlsanity.com/paper/2109.02862/) (2021)
- [Fourier series weight in quantum machine learning](https://mlsanity.com/paper/2302.00105/) (2023)
- [Quantum Machine Learning hyperparameter search](https://mlsanity.com/paper/2302.10298/) (2023)

## Abstract (arXiv)

Convolutional Neural Networks (CNN) are used mainly to treat problems with many images characteristic of Deep Learning. In this work, we propose a hybrid image classification model to take advantage of quantum and classical computing. The method will use the potential that convolutional networks have shown in artificial intelligence by replacing classical filters with variational quantum filters. Similarly, this work will compare with other classification methods and the system's execution on different servers. The algorithm's quantum feasibility is modelled and tested on Amazon Braket Notebook instances and experimented on the Pennylane's philosophy and framework.