Applied Quantum Machine Learning for Image Classification

[amacaluso]

Description

In the last few years, many Quantum Machine Learning (QML) algorithms have been proposed in the literature, promising several theoretical advantages with respect to their classical counterparts. However, when it comes to actual implementation to solve typical data analysis problems, there are many technical issues to face.

In this project, the idea is to write one or more tutorials to train and test a custom Quantum ML model in a typical Data Science scenario for an image classification problem. The purpose is twofold: first, help practitioners in Machine Learning (ML) to approach a typical classification problem where a quantum-classical variational algorithm is employed as an ML model. On the other hand, to provide a guide to people in the Quantum Computing (QC) community to test variational algorithms on a specific, custom dataset.

Mentor/s

<Are you able to mentor this project?>

Type of participant

People with a background in Statistics or Machine Learning

Number of participants

1-2

Deliverable

One or more technical blog about QML and/or a chapter of the qiskit textbook

[kareem1925]

I would love to help and participate