Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search
Summary: Extends RaBitQ to B bits per dimension for flexible, high-rate quantization in high-dimensional Euclidean ANN. Preserves RaBitQ's asymptotically optimal space-error guarantees, with efficient implementations; experiments show superior accuracy and speed at the same memory. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jianyang Gao (Nanyang Technological University)
- 2. Yutong Gou (Nanyang Technological University)
- 3. Yuexuan Xu (Nanyang Technological University)
- 4. Yongyi Yang (University of Michigan)
- 5. Cheng Long (Nanyang Technological University)
- 6. Raymond Chi-Wing Wong (Hong Kong University of Science and Technology)
BibTeX Citation
@inproceedings{gao_sigmod25,
title = {{Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search}},
author = {Gao, Jianyang and Gou, Yutong and Xu, Yuexuan and Yang, Yongyi and Long, Cheng and Wong, Raymond Chi-Wing},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725413},
url = {https://dl.acm.org/doi/10.1145/3725413},
year = {2025}
}
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