It is known since the work of [Aaronson and Ambainis, 2014] that for any permutation symmetric function f, the quantum query complexity is at most polynomially smaller than the classical randomized query complexity, more precisely that R(f) = O~(Q^7(f)). In this paper, we improve this result and show that R(f) = O(Q^3(f)) for a more general class of symmetric functions. Our proof is constructive and relies largely on the quantum hardness of distinguishing a random permutation from a random function with small range from Zhandry [Zhandry, 2015].
It is known since the work of [Aaronson and Ambainis, 2014] that for any permutation symmetric function f, the quantum query complexity is at most polynomially smaller than the classical randomized query complexity, more precisely that R(f) = O~(Q^7(f)). In this paper, we improve this result and show that R(f) = O(Q^3(f)) for a more general class of symmetric functions. Our proof is constructive and relies largely on the quantum hardness of distinguishing a random permutation from a random function with small range from Zhandry [Zhandry, 2015].
It is known since the work of [AA14] that for any permutation symmetric function f, the quantum query complexity is at most polynomially smaller than the classical randomized query complexity, more precisely that R(f)=O(Q7(f)). In this paper, we improve this result and show that R(f)=O(Q3(f)) for a more general class of symmetric functions. Our proof is constructive and relies largely on the quantum hardness of distinguishing a random permutation from a random function with small range from Zhandry [Zha15].
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 problem's 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 $\Omega(N^{1/5}/\polylog N)$ queries to the input function. Our lower bound proof uses a combination of the Collision Problem lower bound and Ambainis's adversary theorem.
Watrous conjectured that the randomized and quantum query complexities of symmetric functions are polynomially equivalent, which was resolved by Aaronson & Ambainis (2014) and was later improved by Chailloux (2019) and Ben-David et al. (2020). This paper explores a fine-grained version of the Watrous conjecture, including the randomized and quantum algorithms with success probabilities arbitrarily close to 1/2 1 / 2 . Our contributions include the following: 1. We analyze the optimal success probabilities of quantum and randomized query algorithms of two fundamental partial symmetric Boolean functions given a fixed number of queries. 2. We establish that for any total symmetric Boolean function f f , if a quantum algorithm uses T T queries to compute f f with success probability 1/2+β 1 / 2 + β , then there exists a randomized algorithm using O(T2) O ( T 2 ) queries to compute f f with success probability 1/2+Ωδβ2 1 / 2 + Ω δ β 2 on a 1−δ 1 - δ fraction of inputs, where β,δ β , δ can be arbitrarily small positive values. Moreover, we prove a randomized version of Aaronson-Ambainis Conjecture (Aaronson & Ambainis 2014) for symmetric Boolean functions in the regime where the success probability of algorithms can be arbitrarily close to 1/2. 3. We present tight polynomial equivalence for several fundamental complexity measures of partial symmetric Boolean functions.
A collision for a function f is two distinct inputs x1=x2 such that f outputs the same value on both inputs: f(x1)=f(x2). The quantum query complexity of finding collisions has been shown~\cite{BHT1997,AS2004, Ambainis05, Kutin05} in some settings to be Θ(N1/3); however, these results do not apply to random functions. The issues are two-fold. First, the Ω(N1/3) lower bound only applies when the domain is no larger than the co-domain, which precludes many of the cryptographically interesting applications. Second, most of the results in the literature only apply to r-to-1 functions, which are quite different from random functions. Understanding the collision problem for random functions is of great importance to cryptography, and we seek to fill the gaps of knowledge for this problem. To that end, we prove that, as expected, a quantum query complexity of Θ(N1/3) holds for all interesting domain and co-domain sizes. Our proofs are simple, and combine existing techniques with several novel tricks to obtain the desired results. Using our techniques, we also give an optimal Ω(M1/3) lower bound for the set equality problem. This lower bound can be used to improve the relationship between classical randomized query complexity and quantum query complexity for so-called permutation-symmetric functions.
Smoothed analysis is a central framework in classical algorithms for explaining the performance of algorithms beyond the worst case, often explaining why algorithms perform well in practice. We initiate a systematic study of its quantum counterpart and show the following results. (1) We show that there is a total function whose smoothed quantum query complexity is exponentially smaller than its classical query complexity. (2) We give near-tight characterizations of smoothed randomized and quantum query complexities for symmetric Boolean functions, unifying the worst-case complexity results of [Beals et al, FOCS'98] and average-case complexity results of [Ambainis and de Wolf, STACS'00]. (3) We study string problems such as pattern matching and edit distance and, in various regimes, give polynomial to superpolynomial quantum speedups. Our main technical ingredients include a near-tight quantum algorithm for ε-approximating the number of collisions between two non-repetitive strings, improving the result of Le Gall and Ng [QIC'22]. Together, our results show that smoothing can reveal larger quantum speedups than worst-case analysis suggests, opening a path towards quantum advantage on more realistic inputs.