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This research establishes an exponential quantum advantage in property testing boolean functions with specific Fourier dimension constraints, outperforming classical limits by orders of magnitude. The study also tightens the theoretical gap between classical upper and lower bounds for this specific computational task.
Surse care acoperăarXiv, Quantum Zeitgeist
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Exponential Quantum Advantage in Testing Fourier Dimensionality

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.25816v1 Announce Type: new Abstract: A boolean function $f$ has Fourier dimension $k$ if its nonzero Fourier coefficients span a subspace of dimension $k$. We consider the property testing task of determining whether a function has Fourier dimension at most $k$, or is $\epsilon$-far from being so. We show that there is a $\Theta(k)$ quantum property tester for this problem. With Gopalan \etal's classical lower bound of $\Omega(2^{k/2})$ for this task, this demonstrates an exponential quantum advantage for this task. We complement this result with a $\tilde{O}(2^{k/2}/\epsilon)$ classical tester, improving the best known upper bound and thus showing the prior lower bound is essentially tight.

Abstract:A boolean function $f$ has Fourier dimension $k$ if its nonzero Fourier coefficients span a subspace of dimension $k$. We consider the property testing task of determining whether a function has Fourier dimension at most $k$, or is $\epsilon$-far from being so. We show that there is a $\Theta(k)$ quantum property tester for this problem. With Gopalan \etal's classical lower bound of $\Omega(2^{k/2})$ for this task, this demonstrates an exponential quantum advantage for this task. We complement this result with a $\tilde{O}(2^{k/2}/\epsilon)$ classical tester, improving the best known upper bound and thus showing the prior lower bound is essentially tight.

Quantum Physics (quant-ph); Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS)

arXiv:2609.25816 [quant-ph]

(or arXiv:2609.25816v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.25816 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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