TurboQuant encoding for Vectors

[connortsui20]

Continuation of #7167, authored by @lwwmanning

Summary

Lossy quantization for vector data (e.g., embeddings) based on TurboQuant (https://arxiv.org/abs/2504.19874). Supports both MSE-optimal and inner-product-optimal (Prod with QJL correction) variants at 1-8 bits per coordinate.

Key components:

• Single TurboQuant array encoding with optional QJL correction fields, storing quantized codes, norms, centroids, and rotation signs as children.
• Structured Random Hadamard Transform (SRHT) for O(d log d) rotation, fully self-contained with no external linear algebra library.
• Max-Lloyd centroid computation on Beta(d/2, d/2) distribution.
• Approximate cosine similarity and dot product compute directly on quantized arrays without full decompression.
• Pluggable TurboQuantScheme for BtrBlocks, exposed via WriteStrategyBuilder::with_vector_quantization().
• Benchmarks covering common embedding dimensions (128, 768, 1024, 1536).

Also refactors CompressingStrategy to a single constructor, and adds vortex_tensor::initialize() for session registration of tensor types, encodings, and scalar functions.

API Changes

Adds a new TurboQuant encoding + some other things. TODO

Testing

TODO