Learning with Quantum Computers

This is the repository for the project "Learning with Quantum Computers" as a part of Winter in Data Science (WiDS) 2022 at IIT Bombay.

Weekwise Plan

This project will be four weeks long. The brief week-wise breakdown is as follows,

1. Week One: Here, you will spend time on learning and/or brushing up on the basic theory of Quantum Computing. The primary text will be the book by Nielsen and Chuang. For the different sub-projects, the alloted reading is as follows,

• QML Implementation

  • Necessary:

    • Chapter 2 (upto and including section 2.2)
    • Chapter 4 (upto and including section 4.6)

  • Optional:

    • Rest of Chapter 2
    • Chapter 5, 6

• QML Literature Review

  • Necessary: Chapter 2, 4, 5, 6
  • Optional: Chapter 8 (Quantum Operations), 9 (Distance Measures)

• QRL (both)

  • Necessary: Chapter 2, 4, 5, 6 (for mentees doing the literature review, chapter 8 might also be useful)

Please find the implementation problems for the first week here.

2. Week Two: Happy new year! This week will be an introduction to the theory of quantum machine learning. The primary references will be An Introduction to QML (Schuld et al.) and An Introduction to QML for engineers (Simeone). After establishing an understanding of the basics of QML, we will move onto some implementation. For the same, please go through the Basic Qubit Rotation Tutorial by Pennylane. Feel free to tinker with pennylane and explore the tutorials.

3. Week Three: This week, we will culminate all that we have learnt, and implement a non-trivial QML algorithm. We will do this by first reading the paper that introduces that algorithm, and then implementing it. Now, within QML there are several fields worth exploring -- from VQEs and QAOAs to Quantum GANs. Some of these can be found in the Pennylane demos. Please go through them, google a bit, and find out what interests you. Fix a topic, find a paper on it, and start the reading and subsequent implementation process. If you need a refresher on ML, go through the resources here.

Note Note that for the completion of the WiDS project, a report is necessary. While the report format is up to you, we recommend writing the report using LaTeX. This can essentially be where you note down things which you do on a weekly basis. For the lit review people, this report should preferably be in a paper format (for instance, IEEE, or APS REVTeX). Other people can also follow this.