QCBM Tutorial

[Gopal-Dahale]

Title:
Quantum Circuit Born Machines
Summary:
Introduces the ideas of Quantum Circuit Born Machines (QCBMs) along with its gradient-based training. Applies QCBM to learn bars and stripes and two peaks dataset.
Relevant references:

Differentiable Learning of Quantum Circuit Born Machine

Possible Drawbacks:

Related GitHub Issues:

---

If you are writing a demonstration, please answer these questions to facilitate the marketing process.

• GOALS — Why are we working on this now?: The purpose is to use PennyLane to implement a popular algorithm in unsupervised generative modelling based on the paper "Differentiable Learning of Quantum Circuit Born Machine".

• AUDIENCE — Who is this for?: The demo provides a gentle introduction to QCBMs, making it suitable for beginners. It also targets individuals interested in generative modelling with quantum algorithms.

• KEYWORDS — What words should be included in the marketing post?: QCBM, QML, MMD, Gradient-based Optimization

• Which of the following types of documentation is most similar to your file?
  (more details here)

• Tutorial
• Demo
• How-to

[github-actions]

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Deployment Info:

• Pull Request ID: 1053
• Deployment SHA: 59d04e85169384d9b8502569ad4ad5c9decd608d
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[KetpuntoG]

Hello! Good job here @Gopal-Dahale !
Is it ready for us to review?
We are short of time during these next two weeks but we will get to it as soon as we can 💪

[Gopal-Dahale]

@KetpuntoG Yes it is ready for review.

[KetpuntoG]

Great job! Thanks for working on this 😊
Here I share with you a first round of comments.

Although all the content looks good, I am convinced that we can make this demo shine much more 🚀Also I would try to make the structure follow a more continuous thread instead of marking epigraphs like "Loss" or "Gradient".

This demo is a great reference for that: https://pennylane.ai/qml/demos/tutorial_intro_qsvt/

As you can see in that demo, the code is quite simplified and makes use of comments but not excessively. I'm sure you can get some good inspiration from the style 💪

[KetpuntoG]

Thank you very much for all the changes, it looks great!

I will talk to our designer to create the thumbnail. Do you have any suggestions on how it should look like?

Once you work on my last comments, I will assign a colleague for the second review :)

[Gopal-Dahale]

I will talk to our designer to create the thumbnail. Do you have any suggestions on how it should look like?

Maybe a quantum circuit outputting a probability distribution. something similar to the quantum gan thumbnail. This is just a suggestion. I think PennyLane's design team is already well-versed and creative 😅.

[alvaro-at-xanadu]

Hey @Gopal-Dahale!

I'll be doing the second review for your demo. I'll get back to you by Friday. Looking forward to reading it 🤩!

[alvaro-at-xanadu]

Amazing job! Results are super good and plotted in an intuitive way! Not many things to fix except for some typos, a bit of grammar, and maybe some clarity here and there 🚀 Will be ready to go soon ⭐

[KetpuntoG]

Great job! Once I update the thumbnail, it's ready to publish 😄
I'll talk to Marketing to see if we can find a slot for mid-May. Congratulations on your contribution! 🚀

[Gopal-Dahale]

Thank you @KetpuntoG. @KetpuntoG and @alvaro-at-xanadu there are a few conversations which are yet to be resolved. Can you take a look at them? I have left some comments.

[KetpuntoG]

I spoke with marketing and the demo will be released in mid-May, thanks!

[KetpuntoG]

🚀 Looks great

[Gopal-Dahale]

@alvaro-at-xanadu what do you think?

done done