The quantum GapSVP hardness conjecture

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Let Λ\Lambda be a lattice of dimension dd, and let λ1(Λ)\lambda_1(\Lambda) denote the ℓ2\ell_2-norm of its shortest nonzero vector. In the promise problem GapSVP\mathrm{GapSVP}, given a lattice Λ\Lambda and t∈Rt\in\mathbb{R}, one must distinguish λ1(Λ)<t\lambda_1(\Lambda)<t from λ1(Λ)≥α(d)t\lambda_1(\Lambda)\geq \alpha(d)t.

Quantum GapSVP hardness conjecture. There is no polynomial-time quantum algorithm that solves GapSVP\mathrm{GapSVP} to within polynomial factors.

This conjectured hardness is used as a cryptographic and algorithmic-theory basis for the paper's learning hardness results. The source attributes the conjecture to the belief that no polynomial-factor quantum algorithm for GapSVP exists, while noting that known algorithms such as LLL achieve only an exponential approximation factor.

References

Primary source

Shuchen Li, Ilias Zadik and Manolis Zampetakis, “On the Hardness of Learning One Hidden Layer Neural Networks”, arXiv:2410.03477 (2024).

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