Quantum Enhanced Filter: QFilter

TL;DR

This paper introduces a hybrid quantum-classical image classification model that replaces classical filters in CNNs with variational quantum filters, aiming to leverage quantum computing's potential for improved AI performance.

Contribution

It proposes a novel hybrid model using variational quantum filters in CNNs and evaluates its feasibility and performance on different platforms and servers.

Findings

Quantum feasibility demonstrated on Amazon Braket

Hybrid model shows potential advantages over classical methods

Experimental results indicate promising performance of quantum filters

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.