Fast Quantized Johnson$-$Lindenstrauss Transforms
Quantization has emerged as one of the primary techniques for reducing the memory footprint of large language models during inference. A prominent example is Quantized Johnson$-$Lindenstrauss (QJL), which compresses KV caches using 1-bit asymmetric quantization while approximately preserving the inner products required by the attention mechanism. Recent theoretical and practical work aims at replacing the dense Gaussian projections used by the aforementioned transforms with more structured embeddings $-$ such as compositions of randomized Hadamard transforms $-$ while retaining the same embedding dimension. In this paper, we introduce two constructions based on a composition of randomized...
| Authors | Angelos Korakitis, Vasileios Nakos |
|---|---|
| Primary category | cs.LG |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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