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Quadratic extractors achieve polylog entropy for quadratic sources

Original title: Quadratic Extractors

arXiv cs.CRby Gautam Chandrasekaran ()1 min read
AI summary · x-ai/grok-4.7Summaries by Vani AI

New explicit construction works for min-entropy k=polylog(n) on quadratic sources, bypassing the sumset extractor barrier for 2-local sources.

A quadratic source is generated by applying n quadratics to a hidden uniform string in F_2^n. Previous explicit extractors required min-entropy roughly k=n-√(log n). The new construction succeeds at k=polylog(n). For 2-local sources, where each output bit depends on two hidden bits, it achieves entropy below √n, which was previously an open problem because such sources cannot be reduced to sumset extractors. The extractor bypasses this barrier with a construction that goes beyond sumset extraction.

Key facts
  • First explicit extractor for quadratic sources at polylog(n) entropy
  • Works for general quadratic sources and 2-local sources
  • Avoids black-box reduction to sumset extractors
  • Explicit construction for k=polylog(n)
  • Improves on previous min-entropy threshold
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From the original article

1 min read at arxiv.org

A quadratic source is a random variable $X\sim\mathbb{F}_2^n$ generated by applying $n$ quadratics to a hidden uniform string. Previously, the best-known explicit extractor for this model required min-entropy roughly $k=n-\sqrt{\log n}$. In this paper, we give an explicit construction for min-entropy $k=\mathrm{polylog}(n)$.

arXiv cs.CR

By Gautam Chandrasekaran · arxiv.org

Read the full article at arXiv cs.CR

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