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- W3049592525 abstract "Aaronson and Ambainis (SICOMP `18) showed that any partial function on $N$ bits that can be computed with an advantage $delta$ over a random guess by making $q$ quantum queries, can also be computed classically with an advantage $delta/2$ by a randomized decision tree making ${O}_q(N^{1-frac{1}{2q}}delta^{-2})$ queries. Moreover, they conjectured the $k$-Forrelation problem -- a partial function that can be computed with $q = lceil k/2 rceil$ quantum queries -- to be a suitable candidate for exhibiting such an extremal separation. We prove their conjecture by showing a tight lower bound of $widetilde{Omega}(N^{1-1/k})$ for the randomized query complexity of $k$-Forrelation, where the advantage $delta = 2^{-O(k)}$. By standard amplification arguments, this gives an explicit partial function that exhibits an $O_epsilon(1)$ vs $Omega(N^{1-epsilon})$ separation between bounded-error quantum and randomized query complexities, where $epsilon>0$ can be made arbitrarily small. Our proof also gives the same bound for the closely related but non-explicit $k$-Rorrelation function introduced by Tal (FOCS `20). Our techniques rely on classical Gaussian tools, in particular, Gaussian interpolation and Gaussian integration by parts, and in fact, give a more general statement. We show that to prove lower bounds for $k$-Forrelation against a family of functions, it suffices to bound the $ell_1$-weight of the Fourier coefficients between levels $k$ and $(k-1)k$. We also prove new interpolation and integration by parts identities that might be of independent interest in the context of rounding high-dimensional Gaussian vectors." @default.
- W3049592525 created "2020-08-21" @default.
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- W3049592525 date "2020-08-16" @default.
- W3049592525 modified "2023-09-27" @default.
- W3049592525 title "$k$-Forrelation Optimally Separates Quantum and Classical Query Complexity" @default.
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