[MRG] Add QSW sampling for sliced Wasserstein

[Samuel-Vangu]

Types of changes

• New feature
• Documentation update
• Tests

Motivation and context / Related issue

Closes #835

This PR adds Quasi-Monte Carlo (QMC) sampling of projection directions to the Sliced Wasserstein module.

Currently, sliced_wasserstein_distance samples projection directions uniformly at random, corresponding to standard Monte Carlo sampling. This PR adds two alternatives based on the generalized spiral point construction described in [Nguyen, Bariletto & Ho (2024)](https://arxiv.org/abs/2309.11713):

• sampling_slices="qsw": deterministic Quasi-Sliced Wasserstein (QSW) projection directions.
• sampling_slices="rqsw": Randomized QSW (RQSW), obtained by applying a random rotation to the deterministic spiral point set.

The new sampling methods are currently limited to 3D, while the existing "uniform" sampling remains the default.

The implementation also exposes get_projections_spiral and updates the documentation, README references, release notes, and adds a 3D example.

How has this been tested (if it applies)

The changes have been tested with:

• pre-commit run --all-files — all checks pass.

• pytest test/sliced/test_sliced_distances.py — 62 tests passed.

• Added tests covering:

  • deterministic spiral projections;
  • randomized projections and sphere preservation;
  • seed reproducibility;
  • invalid dimensions and sampling methods;
  • QSW/RQSW with NumPy, JAX, PyTorch and TensorFlow backends;
  • consistency of deterministic QSW across backends;
  • QSW approximation compared with uniform Monte Carlo sampling in 3D.

PR checklist

• I have read the [CONTRIBUTING](https://pythonot.github.io/contributing.html) document.
• The documentation is up-to-date with the changes I made (check build artifacts).
• All tests passed, and additional code has been covered with new tests.
• I have added the PR and Issue fix to the RELEASES.md file.

[Samuel-Vangu]

Hi @rflamary, @clbonet,

This one is ready for your review whenever you have time. Thanks!