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Foundations of Physics

Quantum Communication Complexity

Gilles Brassard A

Published Oct 24, 2003• 141 citations
Publisher
DOI
10.1023/a:1026009100467

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We prove lower bounds on the bounded error quantum communication complexity. Our methods are based on the Fourier transform of the considered functions. First we generalize a method for proving classical communication complexity lower bounds developed by Raz [Comput. Complexity, 5 (1995), pp. 205–221] to the quantum case. Applying this method, we give an exponential separation between bounded error quantum communication complexity and nondeterministic quantum communication complexity. We develop several other lower bound methods based on the Fourier transform, notably showing that $\sqrt{\bar{s}(f)/\log n}$, for the average sensitivity $\bar{s}(f)$ of a function f, yields a lower bound on the bounded error quantum communication complexity of $f((x \wedge y)\oplus z)$, where x is a Boolean word held by Alice and $y,z$ are Boolean words held by Bob. We then prove the first large lower bounds on the bounded error quantum communication complexity of functions, for which a polynomial quantum speedup is possible. For all the functions we investigate, the only previously applied general lower bound method based on discrepancy yields bounds that are $O(\log n)$.

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We study how symmetry affects quantum advantage in two-party communication complexity. For any partial function fff on length-nnn strings over a fixed alphabet of size qqq, invariant under simultaneous coordinate permutations, we prove Rpub(f)=Oq(tlog⁡(2+n/t))R^{\mathrm{pub}}(f)=O_q(t\log(2+n/t))Rpub(f)=Oq​(tlog(2+n/t)), where t=min⁡{n,Q∗(f)2}t=\min\{n,Q^{\ast}(f)^2\}t=min{n,Q∗(f)2}, RpubR^{\mathrm{pub}}Rpub denotes public-coin randomized communication complexity, and Q∗Q^{\ast}Q∗ denotes entanglement-assisted quantum communication complexity. This bound is tight for set disjointness and extends the binary-alphabet result of Guan et al. with a smaller logarithmic overhead. The exponent of the logarithmic factor cannot be reduced by any fixed positive amount: for every fixed ε∈(0,1)\varepsilon\in(0,1)ε∈(0,1), there are binary permutation-invariant partial functions with quantum communication complexity Oε(log⁡log⁡n)O_\varepsilon(\log\log n)Oε​(loglogn) and randomized communication complexity Ωε((log⁡n)1−ε)Ω_\varepsilon((\log n)^{1-\varepsilon})Ωε​((logn)1−ε). We also characterize quantum communication complexity by a combinatorial parameter up to a logarithmic factor. Growing alphabets and graph symmetries admit exponential separations. For every fixed ε∈(0,1)\varepsilon\in(0,1)ε∈(0,1), we exhibit permutation-invariant partial functions on length-nnn strings over an nnn-symbol alphabet with quantum communication complexity Oε(log⁡n)O_\varepsilon(\log n)Oε​(logn) and randomized communication complexity Ωε(n1−ε)Ω_\varepsilon(n^{1-\varepsilon})Ωε​(n1−ε). For graph-invariant partial functions on vvv-vertex graphs, we obtain Oε(log⁡v)O_\varepsilon(\log v)Oε​(logv) quantum versus Ωε(v2−ε)Ω_\varepsilon(v^{2-\varepsilon})Ωε​(v2−ε) randomized communication complexity. All separation protocols require no prior entanglement.
Communication complexity has become a central completity model.In that model, we count the amount of communication bits needed between two parties in order to solve certain computational problems.We show that for certain communication complezity problems quantum communication protocols are exponentially faster than classical ores.More explicitly, we give an example for a communication complexity relation (0~ promise problem) P such that: 1) The quantum communication complexity of P is O(log m). 2) The classical probabilistic communication complexity of P is Q(m'l'/ logm).(where m is the length of the inputs).This gives an ezponential gap between quantum communication complexity and classical probabilistic communication complexity.Only a quadratic gap was previously known.Our problem P is of geometrical nature, and is a finite precision variation of the following problem: Player I gets as input a unit vector z E R" and two orthogonal subspaces MO, MI c R". Player II gets as input an orthogonal matrixT : R" + R".Their goal is to answer 0 if T(x) E MO and 1 if T(x) E M,, (and any an~luer in any other case).We give an almost tight analysis for the quantum communication complexity and for the classical-probabilistic communication complexity of this problem.
We prove lower bounds on the bounded error quantum communication complexity. Our methods are based on the Fourier transform of the considered functions. First we generalize a method for proving classical communication complexity lower bounds developed by Raz [Comput. Complexity, 5 (1995), pp. 205–221] to the quantum case. Applying this method, we give an exponential separation between bounded error quantum communication complexity and nondeterministic quantum communication complexity. We develop several other lower bound methods based on the Fourier transform, notably showing that sˉ(f)/log⁡n\sqrt{\bar{s}(f)/\log n}sˉ(f)/logn​, for the average sensitivity sˉ(f)\bar{s}(f)sˉ(f) of a function f, yields a lower bound on the bounded error quantum communication complexity of f((x∧y)⊕z)f((x \wedge y)\oplus z)f((x∧y)⊕z), where x is a Boolean word held by Alice and y,zy,zy,z are Boolean words held by Bob. We then prove the first large lower bounds on the bounded error quantum communication complexity of functions, for which a polynomial quantum speedup is possible. For all the functions we investigate, the only previously applied general lower bound method based on discrepancy yields bounds that are O(log⁡n)O(\log n)O(logn).