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arXiv:1401.3826

Adversary Lower Bound for Element Distinctness with Small Range

Ansis Rosmanis

Published Jan 16, 2014• 3 citations
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Abstract

The Element Distinctness problem is to decide whether each character of an input string is unique. The quantum query complexity of Element Distinctness is known to be $Θ(N^{2/3})$; the polynomial method gives a tight lower bound for any input alphabet, while a tight adversary construction was only known for alphabets of size $Ω(N^2)$. We construct a tight $Ω(N^{2/3})$ adversary lower bound for Element Distinctness with minimal non-trivial alphabet size, which equals the length of the input. This result may help to improve lower bounds for other related query problems.

DOI
10.48550/arxiv.1401.3826

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The Element Distinctness problem is to decide whether each character of an input string is unique. The quantum query complexity of Element Distinctness is known to be Θ(N2/3)Θ(N^{2/3})Θ(N2/3); the polynomial method gives a tight lower bound for any input alphabet, while a tight adversary construction was only known for alphabets of size Ω(N2)Ω(N^2)Ω(N2). We construct a tight Ω(N2/3)Ω(N^{2/3})Ω(N2/3) adversary lower bound for Element Distinctness with minimal non-trivial alphabet size, which equals the length of the input. This result may help to improve lower bounds for other related query problems.
An open problem that is widely regarded as one of the most important in quantum query complexity is to resolve the quantum query complexity of the k-distinctness function on inputs of size N. While the case of k=2 (also called Element Distinctness) is well-understood, there is a polynomial gap between the known upper and lower bounds for all constants k>2. Specifically, the best known upper bound is O(N^{(3/4)-1/(2^{k+2}-4)}) (Belovs, FOCS 2012), while the best known lower bound for k >= 2 is Omega(N^{2/3} + N^{(3/4)-1/(2k)}) (Aaronson and Shi, J.~ACM 2004; Bun, Kothari, and Thaler, STOC 2018). For any constant k >= 4, we improve the lower bound to Omega(N^{(3/4)-1/(4k)}). This yields, for example, the first proof that 4-distinctness is strictly harder than Element Distinctness. Our lower bound applies more generally to approximate degree. As a secondary result, we give a simple construction of an approximating polynomial of degree O(N^{3/4}) that applies whenever k <= polylog(N).
We prove a quantum query lower bound Ω(n^{(d+1)/(d+2)}) for the problem of deciding whether an input string of size n contains a k-tuple which belongs to a fixed orthogonal array on k factors of strength d<=k-1 and index 1, provided that the alphabet size is sufficiently large. Our lower bound is tight when d=k-1. The orthogonal array problem includes the following problems as special cases: k-sum problem with d=k-1, k-distinctness problem with d=1, k-pattern problem with d=0, (d-1)-degree problem with 1<=d<=k-1, unordered search with d=0 and k=1, and graph collision with d=0 and k=2.
The general adversary bound is a semi-definite program that lower-bounds the number of input queries needed by a quantum algorithm to evaluate a function. It is known to be tight up to constant factors for functions (total or partial) with boolean output and binary input alphabet. We show that the general adversary bound is tight for any function whatsoever, with potentially non-boolean input or output alphabets. We also show that quantum query complexity exhibits a remarkable composition property: Q ( f(g(x), . . . , g(x)) ) = O ( Q(f)Q(g) ) for any compatible functions f, g. This was previously known only in the boolean case. Both of these results are obtained by defining a new, but closely related, semi-definite program that we call the witness size. The minimization formulation of the witness size adds constraints compared with the general adversary bound to enable dual solutions to correspond to eigenvalue-zero eigenvectors of certain graphs. While the witness size can be strictly larger than the adversary bound, we show that it can be at most a factor of two larger.
In the thesis, we use a recently developed tight characterisation of quantum query complexity, the adversary bound, to develop new quantum algorithms and lower bounds. Our results are as follows: * We develop a new technique for the construction of quantum algorithms: learning graphs. * We use learning graphs to improve quantum query complexity of the triangle detection and the kkk-distinctness problems. * We prove tight lower bounds for the kkk-sum and the triangle sum problems. * We construct quantum algorithms for some subgraph-finding problems that are optimal in terms of query, time and space complexities. * We develop a generalisation of quantum walks that connects electrical properties of a graph and its quantum hitting time. We use it to construct a time-efficient quantum algorithm for 3-distinctness.