PAPER-50 & PAPER-78: LatentQGAN and BP in NN landscape

[CassNot]

Summary

Reproduced papers documentation for LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder
& Barren plateaus in quantum neural network training landscapes

This includes:

• gallery cards
• citations

Related Issue

Type of change

• Bug fix
• New feature
• Documentation update
• Refactor / Cleanup
• Performance improvement
• CI / Build / Tooling
• Breaking change (requires migration notes)

How to test / How to run

1. Command lines

make -C docs clean html

Documentation

• User docs updated (Sphinx)
• Examples / notebooks updated
• Docstrings updated
• Updated the API

Checklist

• PR title includes Jira issue key (e.g., PML-126)
• "Related Jira ticket" section includes the Jira issue key (no URL)
• Code formatted (ruff format)
• Lint passes (ruff)
• Static typing passes (mypy) if applicable
• Unit tests added/updated (pytest)
• Tests pass locally (pytest)
• Tests pass on GPU (pytest)
• Test coverage not decreased significantly
• Docs build locally if affected (sphinx)
• With this command: SPHINXOPTS="-W --keep-going -n" make -C docs clean html the docs are built without any warning or errors.
• New public classes/methods/packages are added in the API following the methodology presented in other files.
• Dependencies updated (if needed) and pinned appropriately
• PR description explains what changed and how to validate it