Comprehensive comparison of QML models

[adekusar-drl]

Description

This project aims at investigating various factors that can affect performance of QML models. We start from a choice of a small dataset and train a classical model as a reference baseline. Then we explore different ways of data encoding (embeddings/feature maps) and ansatze and how they affect performance of QML models. In the same time we explore other characteristics of the models such that number of parameters, CNOTs, layers. This work will be conducted on a simulator. Then, we'll try to train the best models on real hardware and find out the limits of the hardware for QML tasks. THis

The project should be straight-forward and does not require extensive knowledge of quantum computing and/or machine learning. But in the same time this project requires very careful and meticulous work to be sure the obtained results are correct and exhaustive.

Deliverables

A quick report or a notebook with the results obtained in the project.

Mentors details

• Mentor 1
  • Name: Anton Dekusar
  • GitHub ID: https://github.com/adekusar-drl
  • What they do: Contributor and maintainer of Qiskit Machine Learning

Number of mentees

1

Type of mentees

• Mentee 1
  • Required:
  • Knowledge of classical machine learning
  • Basic knowledge of quantum machine learning
  • Good writing skills
  • Patience when training a model on a real hardware :)

[ShivaniRajput11]

Hello @adekusar-drl . I am particularly excited about the opportunity to work with a small dataset and explore different ways of data encoding.

I have understanding of classical machine learning and has completed certification course on quantum machine learning. Have participated in Hackathons too. I am also proficient in Python and have experience working with Qiskit.

In addition to my technical skills, I have strong writing skills and am confident that I can provide clear and concise documentation of my work. I am also patient,when it comes to working with real hardware, and I understand the importance of carefully analyzing results to ensure their accuracy.

I am eager to learn and open to guidance .

I look forward to the opportunity to work with you on this project.

[Qcatty]

Hi, I am interested about this project!

[ArupjyotiPathak]

Dear Conserned, I am interested to join this project. I have relevant experience in quantum machine learning and also have sound knowledge of IBM quantum and pannylane quantum hardwares. My LinkedIn profile is: https://www.linkedin.com/in/arupjyoti-pathak-2a35a1184

Looking forward to hearing from you soon.

[EACMichiels]

Dear Anton, Dear Shivani, I have had too much workload to join from the start. But I would be eager to see the results of this work and potentially review the work done and provide feedback. Let me know if I still can help please. @adekusar-drl @ArupjyotiPathak @ShivaniRajput11

[adekusar-drl]

The project was left unassigned, no results to share.