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Exponential separation of quantum and classical one-way communication complexity

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TLDR
The Hidden Matching Problem HMn is defined and it is proved that any randomized linear one-way protocol with bounded error for this problem requires Ω(√[3] n log n) bits of communication.
Abstract
We give the first exponential separation between quantum and bounded-error randomized one-way communication complexity. Specifically, we define the Hidden Matching Problem HMn: Alice gets as input a string x ∈ (0, 1)n and Bob gets a perfect matching M on the n coordinates. Bob's goal is to output a tuple [i,j,b] such that the edge (i,j) belongs to the matching M and b = xi ⊕ xj. We prove that the quantum one-way communication complexity of HMn is O(log n), yet any randomized one-way protocol with bounded error must use Ω(√n) bits of communication. No asymptotic gap for one-way communication was previously known. Our bounds also hold in the model of Simultaneous Messages (SM) and hence we provide the first exponential separation between quantum SM and randomized SM with public coins.For a Boolean decision version of HMn, we show that the quantum one-way communication complexity remains O(log n) and that the 0-error randomized one-way communication complexity is Ω(n). We prove that any randomized linear one-way protocol with bounded error for this problem requires Ω(√[3] n log n) bits of communication.

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Citations
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Journal ArticleDOI

Nonlocality and communication complexity

TL;DR: The area of quantum communication complexity is reviewed, and it is shown how it connects the foundational physics questions regarding non-locality with those of communication complexity studied in theoretical computer science.
Book ChapterDOI

Communication Complexity: Basics

Journal ArticleDOI

Exponential Communication Complexity Advantage from Quantum Superposition of the Direction of Communication.

TL;DR: This work presents a tripartite communication task for which such a superposition of the direction of communication allows for an exponential saving in communication, compared to one-way quantum (or classical) communication; the advantage also holds when the authors allow for protocols with bounded error probability.
Journal ArticleDOI

Quantum Pseudo-Telepathy

TL;DR: Quantum communication complexity as mentioned in this paper uses quantum mechanics to reduce the amount of communication that would be classically required, which is an area of classical computer science that aims at quantifying the number of communication necessary to solve distributed computational problems, and is concerned with what we can and cannot do with quantum information that goes beyond the abilities of classical information processing devices.
Proceedings ArticleDOI

Exponential separations for one-way quantum communication complexity, with applications to cryptography

TL;DR: In this article, an exponential separation between one-way quantum and classical communication protocols for two-partial Boolean functions was shown for two variants of the Hidden Matching Problem of Bar-Yossef et al. They used the Fourier coefficients inequality of Kahn, Kalai, and Linial.
References
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Book

Quantum Computation and Quantum Information

TL;DR: In this article, the quantum Fourier transform and its application in quantum information theory is discussed, and distance measures for quantum information are defined. And quantum error-correction and entropy and information are discussed.
Journal ArticleDOI

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

TL;DR: In this paper, the authors considered factoring integers and finding discrete logarithms on a quantum computer and gave an efficient randomized algorithm for these two problems, which takes a number of steps polynomial in the input size of the integer to be factored.
Book

Communication Complexity

TL;DR: This chapter surveys the theory of two-party communication complexity and presents results regarding the following models of computation: • Finite automata • Turing machines • Decision trees • Ordered binary decision diagrams • VLSI chips • Networks of threshold gates.
Proceedings ArticleDOI

Some complexity questions related to distributive computing(Preliminary Report)

TL;DR: The quantity of interest, which measures the information exchange necessary for computing f, is the minimum number of bits exchanged in any algorithm.
Proceedings ArticleDOI

Quantum circuit complexity

TL;DR: It is shown that any function computable in polynomial time by a quantum Turing machine has aPolynomial-size quantum circuit, and this result enables us to construct a universal quantum computer which can simulate a broader class of quantum machines than that considered by E. Bernstein and U. Vazirani (1993), thus answering an open question raised by them.