A Classical Quadratic Speedup for Planted $k$XOR

📅 2025-08-12
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🤖 AI Summary
This work addresses the noisy $k$XOR problem—where $k$ is a large constant—and presents the first classical algorithm achieving quadratic speedup, thereby substantially narrowing the quantum advantage from prior quartic to quadratic. Methodologically, it introduces a novel integration of sublinear-time algorithmic frameworks with polynomial anti-concentration phenomena, while strategically leveraging the birthday paradox to design an efficient solver tailored to semi-random noise settings. The resulting algorithm improves upon the best prior classical time complexity by a quadratic factor. Although quantum algorithms retain asymptotic advantages in space complexity, their temporal superiority is materially diminished. This work advances the understanding of the classical–quantum boundary for the $k$XOR problem, offering new insights into the interplay between noise structure, algorithmic primitives, and computational hardness.

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📝 Abstract
A recent work of Schmidhuber et al (QIP, SODA, & Phys. Rev. X 2025) exhibited a quantum algorithm for the noisy planted $k$XOR problem running quartically faster than all known classical algorithms. In this work, we design a new classical algorithm that is quadratically faster than the best previous one, in the case of large constant $k$. Thus for such $k$, the quantum speedup of Schmidhuber et al. becomes only quadratic (though it retains a space advantage). Our algorithm, which also works in the semirandom case, combines tools from sublinear-time algorithms (essentially, the birthday paradox) and polynomial anticoncentration.
Problem

Research questions and friction points this paper is trying to address.

Improves classical algorithm speed for planted kXOR
Reduces quantum speedup gap to quadratic for large k
Works in semirandom cases using sublinear-time techniques
Innovation

Methods, ideas, or system contributions that make the work stand out.

Classical algorithm with quadratic speedup
Uses sublinear-time techniques
Employs polynomial anticoncentration
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