[P] Implemented TurboQuant in Python

Spent ~2 days implementing this paper: TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate Repo: github.com/yashkc2025/turboquant Most quantization stuff I’ve worked with usually falls into one of these: you need calibration data (k-means, clipping ranges, etc.) or you go naive (uniform quant) and take the quality hit This paper basically says: what if we just… don’t do either? The main idea is weirdly simple: take your vector hit it with a random rotation now suddenly the coordinates behave nicely (like ~Gaussian-ish) so you can just do optimal 1D quantization per.