add pvqnn

[elainazhu]

---

Title: Post-Variational Quantum Neural Networks

Summary: In this demo, we discuss “post-variational strategies”, where we take the classical combination of multiple fixed quantum circuits and find the optimal combination through feeding our combinations through a classical multilayer perceptron. We shift tunable parameters from the quantum computer to the classical computer, opting for ensemble strategies when optimizing quantum models.

Relevant references: P.-W. Huang, P. Rebentrost (2023). Post-variational quantum neural networks. arXiv:2307.10560 [quant-ph]

Possible Drawbacks: Scalability.

Related GitHub Issues: NA

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

• GOALS — Why are we working on this now?

Introduce a new architecture for quantum machine learning.

• AUDIENCE — Who is this for?

Academic Researchers and Students, Quantum Technology enthusiasts

• KEYWORDS — What words should be included in the marketing post?

Quantum Machine Learning, Neural Networks, Post-Variational

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

• [ ] Tutorial

• [ v] Demo

• [ ] How-to

[github-actions[bot]]

Thank you for opening this pull request.

You can find the built site at this link.

Deployment Info:

• Pull Request ID: 1154
• Deployment SHA: 51d598904ba17c3f3457b1f05a89f00880773963 (The Deployment SHA refers to the latest commit hash the docs were built from)

Note: It may take several minutes for updates to this pull request to be reflected on the deployed site.