BlockQuant is a new block quantization algorithm on the sphere after random rotation that theoretically improves reconstruction MSE and expected inner-product distortion over EDEN, RabitQ, and TurboQuant.
2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS) , pages=
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Dithered quantization after a single randomized Hadamard transform yields unbiased estimates whose MSE asymptotically equals that of dense random rotations, specifically (π√3/2 + o(1))·4^{-b} for b-bit TurboQuant.
RaBitQ outperforms TurboQuant in most tested settings for inner-product estimation, nearest-neighbor search, and KV cache quantization, while several TurboQuant runtime and recall results could not be reproduced from the released code.
citing papers explorer
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Block-Sphere Vector Quantization
BlockQuant is a new block quantization algorithm on the sphere after random rotation that theoretically improves reconstruction MSE and expected inner-product distortion over EDEN, RabitQ, and TurboQuant.
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Provable Quantization with Randomized Hadamard Transform
Dithered quantization after a single randomized Hadamard transform yields unbiased estimates whose MSE asymptotically equals that of dense random rotations, specifically (π√3/2 + o(1))·4^{-b} for b-bit TurboQuant.
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Revisiting RaBitQ and TurboQuant: A Symmetric Comparison of Methods, Theory, and Experiments
RaBitQ outperforms TurboQuant in most tested settings for inner-product estimation, nearest-neighbor search, and KV cache quantization, while several TurboQuant runtime and recall results could not be reproduced from the released code.