32 research outputs found
An Optimal Lower Bound on the Communication Complexity of Gap-Hamming-Distance
We prove an optimal lower bound on the randomized communication
complexity of the much-studied Gap-Hamming-Distance problem. As a consequence,
we obtain essentially optimal multi-pass space lower bounds in the data stream
model for a number of fundamental problems, including the estimation of
frequency moments.
The Gap-Hamming-Distance problem is a communication problem, wherein Alice
and Bob receive -bit strings and , respectively. They are promised
that the Hamming distance between and is either at least
or at most , and their goal is to decide which of these is the
case. Since the formal presentation of the problem by Indyk and Woodruff (FOCS,
2003), it had been conjectured that the naive protocol, which uses bits of
communication, is asymptotically optimal. The conjecture was shown to be true
in several special cases, e.g., when the communication is deterministic, or
when the number of rounds of communication is limited.
The proof of our aforementioned result, which settles this conjecture fully,
is based on a new geometric statement regarding correlations in Gaussian space,
related to a result of C. Borell (1985). To prove this geometric statement, we
show that random projections of not-too-small sets in Gaussian space are close
to a mixture of translated normal variables
Efficient quantum protocols for XOR functions
We show that for any Boolean function f on {0,1}^n, the bounded-error quantum
communication complexity of XOR functions satisfies that
, where d is the F2-degree of f, and
.
This implies that the previous lower bound by Lee and Shraibman \cite{LS09} is tight
for f with low F2-degree. The result also confirms the quantum version of the
Log-rank Conjecture for low-degree XOR functions. In addition, we show that the
exact quantum communication complexity satisfies , where is the number of nonzero Fourier coefficients of
f. This matches the previous lower bound
by Buhrman and de Wolf \cite{BdW01} for low-degree XOR functions.Comment: 11 pages, no figur
Approximating Approximate Pattern Matching
Given a text of length and a pattern of length , the
approximate pattern matching problem asks for computation of a particular
\emph{distance} function between and every -substring of . We
consider a multiplicative approximation variant of this
problem, for distance function. In this paper, we describe two
-approximate algorithms with a runtime of
for all (constant) non-negative values
of . For constant we show a deterministic
-approximation algorithm. Previously, such run time was known
only for the case of distance, by Gawrychowski and Uzna\'nski [ICALP
2018] and only with a randomized algorithm. For constant we
show a randomized algorithm for the , thereby providing a smooth
tradeoff between algorithms of Kopelowitz and Porat [FOCS~2015, SOSA~2018] for
Hamming distance (case of ) and of Gawrychowski and Uzna\'nski for
distance
Quantum and Classical Communication Complexity of Permutation-Invariant Functions
This paper gives a nearly tight characterization of the quantum communication
complexity of the permutation-invariant Boolean functions. With such a
characterization, we show that the quantum and randomized communication
complexity of the permutation-invariant Boolean functions are quadratically
equivalent (up to a logarithmic factor). Our results extend a recent line of
research regarding query complexity \cite{AA14, Cha19, BCG+20} to communication
complexity, showing symmetry prevents exponential quantum speedups.
Furthermore, we show the Log-rank Conjecture holds for any non-trivial total
permutation-invariant Boolean function. Moreover, we establish a relationship
between the quantum/classical communication complexity and the approximate rank
of permutation-invariant Boolean functions. This implies the correctness of the
Log-approximate-rank Conjecture for permutation-invariant Boolean functions in
both randomized and quantum settings (up to a logarithmic factor).Comment: accepted in STACS 202
A concentration inequality for the overlap of a vector on a large set, with application to the communication complexity of the Gap-Hamming-Distance problem
Given two sets A, B ⊆ R_n, a measure of their correlation is given by the expected squared inner product between random x ϵ A and y ϵ B. We prove an inequality showing that no two sets of large enough Gaussian measure (at least e^(-δn) for some constant δ > 0) can have correlation substantially lower than would two random sets of the same size. Our proof is based on a concentration inequality for the overlap of a random Gaussian vector on a large set.
As an application, we show how our result can be combined with the partition bound of Jain and Klauck to give a simpler proof of a recent linear lower bound on the randomized communication complexity of the Gap-Hamming-Distance problem due to Chakrabarti and Regev
The quantum complexity of approximating the frequency moments
The 'th frequency moment of a sequence of integers is defined as , where is the number of times that occurs in the
sequence. Here we study the quantum complexity of approximately computing the
frequency moments in two settings. In the query complexity setting, we wish to
minimise the number of queries to the input used to approximate up to
relative error . We give quantum algorithms which outperform the best
possible classical algorithms up to quadratically. In the multiple-pass
streaming setting, we see the elements of the input one at a time, and seek to
minimise the amount of storage space, or passes over the data, used to
approximate . We describe quantum algorithms for , and
in this model which substantially outperform the best possible
classical algorithms in certain parameter regimes.Comment: 22 pages; v3: essentially published versio