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Register a new userA quantum lower bound for distinguishing random functions from random permutations
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Henry Yuen
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The problem of distinguishing between a random function and a random permutation on a domain of size $N$ is important in theoretical cryptography, where the security of many primitives depend on the problems hardness. We study the quantum query complexity of this problem, and show that any quantum algorithm that solves this problem with bounded error must make $Omega(N^{1/5}/log N)$ queries to the input function. Our lower bound proof uses a combination of the Collision Problem lower bound and Ambainiss adversary theorem.
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We show that any quantum circuit of treewidth $t$, built from $r$-qubit gates, requires at least $Omega(frac{n^{2}}{2^{O(rcdot t)}cdot log^4 n})$ gates to compute the element distinctness function. Our result generalizes a near-quadratic lower bound for quantum formula size obtained by Roychowdhury and Vatan [SIAM J. on Computing, 2001]. The proof of our lower bound follows by an extension of Nev{c}iporuks method to the context of quantum circuits of constant treewidth. This extension is made via a combination of techniques from structural graph theory, tensor-network theory, and the connected-component counting method, which is a classic tool in algebraic geometry.
The Fourier-Entropy Influence (FEI) Conjecture states that for any Boolean function $f:{+1,-1}^n to {+1,-1}$, the Fourier entropy of $f$ is at most its influence up to a universal constant factor. While the FEI conjecture has been proved for many classes of Boolean functions, it is still not known whether it holds for the class of Linear Threshold Functions. A natural question is: Does the FEI conjecture hold for a `random linear threshold function? In this paper, we answer this question in the affirmative. We consider two natural distributions on the weights defining a linear threshold function, namely uniform distribution on $[-1,1]$ and Normal distribution.
Hr{a}stad showed that any De Morgan formula (composed of AND, OR and NOT gates) shrinks by a factor of $O(p^{2})$ under a random restriction that leaves each variable alive independently with probability $p$ [SICOMP, 1998]. Using this result, he gave an $widetilde{Omega}(n^{3})$ formula size lower bound for the Andreev function, which, up to lower order improvements, remains the state-of-the-art lower bound for any explicit function. In this work, we extend the shrinkage result of Hr{a}stad to hold under a far wider family of random restrictions and their generalization -- random projections. Based on our shrinkage results, we obtain an $widetilde{Omega}(n^{3})$ formula size lower bound for an explicit function computed in $mathbf{AC}^0$. This improves upon the best known formula size lower bounds for $mathbf{AC}^0$, that were only quadratic prior to our work. In addition, we prove that the KRW conjecture [Karchmer et al., Computational Complexity 5(3/4), 1995] holds for inner functions for which the unweighted quantum adversary bound is tight. In particular, this holds for inner functions with a tight Khrapchenko bound. Our random projections are tailor-made to the functions structure so that the function maintains structure even under projection -- using such projections is necessary, as standard random restrictions simplify $mathbf{AC}^0$ circuits. In contrast, we show that any De Morgan formula shrinks by a quadratic factor under our random projections, allowing us to prove the cubic lower bound. Our proof techniques build on the proof of Hr{a}stad for the simpler case of balanced formulas. This allows for a significantly simpler proof at the cost of slightly worse parameters. As such, when specialized to the case of $p$-random restrictions, our proof can be used as an exposition of Hr{a}stads result.
Computing the reversal distances of signed permutations is an important topic in Bioinformatics. Recently, a new lower bound for the reversal distance was obtained via the plane permutation framework. This lower bound appears different from the existing lower bound obtained by Bafna and Pevzner through breakpoint graphs. In this paper, we prove that the two lower bounds are equal. Moreover, we confirm a related conjecture on skew-symmetric plane permutations, which can be restated as follows: let $p=(0,-1,-2,ldots -n,n,n-1,ldots 1)$ and let $$ tilde{s}=(0,a_1,a_2,ldots a_n,-a_n,-a_{n-1},ldots -a_1) $$ be any long cycle on the set ${-n,-n+1,ldots 0,1,ldots n}$. Then, $n$ and $a_n$ are always in the same cycle of the product $ptilde{s}$. Furthermore, we show the new lower bound via plane permutations can be interpreted as the topological genera of orientable surfaces associated to signed permutations.
A classical result for the simple symmetric random walk with $2n$ steps is that the number of steps above the origin, the time of the last visit to the origin, and the time of the maximum height all have exactly the same distribution and converge when scaled to the arcsine law. Motivated by applications in genomics, we study the distributions of these statistics for the non-Markovian random walk generated from the ascents and descents of a uniform random permutation and a Mallows($q$) permutation and show that they have the same asymptotic distributions as for the simple random walk. We also give an unexpected conjecture, along with numerical evidence and a partial proof in special cases, for the result that the number of steps above the origin by step $2n$ for the uniform permutation generated walk has exactly the same discrete arcsine distribution as for the simple random walk, even though the other statistics for these walks have very different laws. We also give explicit error bounds to the limit theorems using Steins method for the arcsine distribution, as well as functional central limit theorems and a strong embedding of the Mallows$(q)$ permutation which is of independent interest.
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