# Quantum Enhanced Filter: QFilter

Source: [Quantum Enhanced Filter: QFilter](<https://www.sota2.com/research/paper/68fa3ecd1e209131ce8e885c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T02:03:20.416Z

Authors: Parfait Atchade-Adelomou, Guillermo Alonso-Linaje

Published: 2021-04-07T22:20:20.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.03418>)

[PDF](<https://arxiv.org/pdf/2104.03418.pdf>)