4,357 research outputs found
Generalizations of the distributed Deutsch-Jozsa promise problem
In the {\em distributed Deutsch-Jozsa promise problem}, two parties are to
determine whether their respective strings are at the {\em
Hamming distance} or . Buhrman et al. (STOC' 98)
proved that the exact {\em quantum communication complexity} of this problem is
while the {\em deterministic communication complexity} is
. This was the first impressive (exponential) gap between
quantum and classical communication complexity.
In this paper, we generalize the above distributed Deutsch-Jozsa promise
problem to determine, for any fixed , whether
or , and show that an exponential gap between exact
quantum and deterministic communication complexity still holds if is an
even such that , where is given. We also deal with a promise version of the
well-known {\em disjointness} problem and show also that for this promise
problem there exists an exponential gap between quantum (and also
probabilistic) communication complexity and deterministic communication
complexity of the promise version of such a disjointness problem. Finally, some
applications to quantum, probabilistic and deterministic finite automata of the
results obtained are demonstrated.Comment: we correct some errors of and improve the presentation the previous
version. arXiv admin note: substantial text overlap with arXiv:1309.773
Communication Complexity of Permutation-Invariant Functions
Motivated by the quest for a broader understanding of communication
complexity of simple functions, we introduce the class of
"permutation-invariant" functions. A partial function is permutation-invariant if for every bijection
and every , it is the case that . Most of the commonly studied functions
in communication complexity are permutation-invariant. For such functions, we
present a simple complexity measure (computable in time polynomial in given
an implicit description of ) that describes their communication complexity
up to polynomial factors and up to an additive error that is logarithmic in the
input size. This gives a coarse taxonomy of the communication complexity of
simple functions. Our work highlights the role of the well-known lower bounds
of functions such as 'Set-Disjointness' and 'Indexing', while complementing
them with the relatively lesser-known upper bounds for 'Gap-Inner-Product'
(from the sketching literature) and 'Sparse-Gap-Inner-Product' (from the recent
work of Canonne et al. [ITCS 2015]). We also present consequences to the study
of communication complexity with imperfectly shared randomness where we show
that for total permutation-invariant functions, imperfectly shared randomness
results in only a polynomial blow-up in communication complexity after an
additive overhead
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