9,630 research outputs found
Quantum Information Complexity and Amortized Communication
We define a new notion of information cost for quantum protocols, and a
corresponding notion of quantum information complexity for bipartite quantum
channels, and then investigate the properties of such quantities. These are the
fully quantum generalizations of the analogous quantities for bipartite
classical functions that have found many applications recently, in particular
for proving communication complexity lower bounds. Our definition is strongly
tied to the quantum state redistribution task.
Previous attempts have been made to define such a quantity for quantum
protocols, with particular applications in mind; our notion differs from these
in many respects. First, it directly provides a lower bound on the quantum
communication cost, independent of the number of rounds of the underlying
protocol. Secondly, we provide an operational interpretation for quantum
information complexity: we show that it is exactly equal to the amortized
quantum communication complexity of a bipartite channel on a given state. This
generalizes a result of Braverman and Rao to quantum protocols, and even
strengthens the classical result in a bounded round scenario. Also, this
provides an analogue of the Schumacher source compression theorem for
interactive quantum protocols, and answers a question raised by Braverman.
We also discuss some potential applications to quantum communication
complexity lower bounds by specializing our definition for classical functions
and inputs. Building on work of Jain, Radhakrishnan and Sen, we provide new
evidence suggesting that the bounded round quantum communication complexity of
the disjointness function is \Omega (n/M + M), for M-message protocols. This
would match the best known upper bound.Comment: v1, 38 pages, 1 figur
Infinite Communication Complexity
Suppose that Alice and Bob are given each an infinite string, and they want
to decide whether their two strings are in a given relation. How much
communication do they need? How can communication be even defined and measured
for infinite strings? In this article, we propose a formalism for a notion of
infinite communication complexity, prove that it satisfies some natural
properties and coincides, for relevant applications, with the classical notion
of amortized communication complexity. More-over, an application is given for
tackling some conjecture about tilings and multidimensional sofic shifts.Comment: First Version. Written from the Computer Science PO
Exponential Separation of Quantum Communication and Classical Information
We exhibit a Boolean function for which the quantum communication complexity
is exponentially larger than the classical information complexity. An
exponential separation in the other direction was already known from the work
of Kerenidis et. al. [SICOMP 44, pp. 1550-1572], hence our work implies that
these two complexity measures are incomparable. As classical information
complexity is an upper bound on quantum information complexity, which in turn
is equal to amortized quantum communication complexity, our work implies that a
tight direct sum result for distributional quantum communication complexity
cannot hold. The function we use to present such a separation is the Symmetric
k-ary Pointer Jumping function introduced by Rao and Sinha [ECCC TR15-057],
whose classical communication complexity is exponentially larger than its
classical information complexity. In this paper, we show that the quantum
communication complexity of this function is polynomially equivalent to its
classical communication complexity. The high-level idea behind our proof is
arguably the simplest so far for such an exponential separation between
information and communication, driven by a sequence of round-elimination
arguments, allowing us to simplify further the approach of Rao and Sinha.
As another application of the techniques that we develop, we give a simple
proof for an optimal trade-off between Alice's and Bob's communication while
computing the related Greater-Than function on n bits: say Bob communicates at
most b bits, then Alice must send n/exp(O(b)) bits to Bob. This holds even when
allowing pre-shared entanglement. We also present a classical protocol
achieving this bound.Comment: v1, 36 pages, 3 figure
Cell-Probe Lower Bounds from Online Communication Complexity
In this work, we introduce an online model for communication complexity.
Analogous to how online algorithms receive their input piece-by-piece, our
model presents one of the players, Bob, his input piece-by-piece, and has the
players Alice and Bob cooperate to compute a result each time before the next
piece is revealed to Bob. This model has a closer and more natural
correspondence to dynamic data structures than classic communication models do,
and hence presents a new perspective on data structures.
We first present a tight lower bound for the online set intersection problem
in the online communication model, demonstrating a general approach for proving
online communication lower bounds. The online communication model prevents a
batching trick that classic communication complexity allows, and yields a
stronger lower bound. We then apply the online communication model to prove
data structure lower bounds for two dynamic data structure problems: the Group
Range problem and the Dynamic Connectivity problem for forests. Both of the
problems admit a worst case -time data structure. Using online
communication complexity, we prove a tight cell-probe lower bound for each:
spending (even amortized) time per operation results in at best an
probability of correctly answering a
-fraction of the queries
On the Amortized Communication Complexity of Byzantine Broadcast
Designing an efficient solution for Byzantine broadcast is an important problem for many distributed computing and cryptographic tasks. There have been many attempts to achieve sub-quadratic communication complexity in several directions, both in theory and practice, all with pros and cons. This paper initiates the study of another attempt: improving the amortized communication complexity of multi-shot Byzantine broadcast. Namely, we try to improve the average cost when we have sequential multiple broadcast instances. We present a protocol that achieves optimal amortized linear complexity under an honest majority. Our core technique is to efficiently form a network for disseminating the sender\u27s message by keeping track of dishonest behaviors over multiple instances. We also generalize the technique for the dishonest majority to achieve amortized quadratic communication complexity
Trading Information Complexity for Error
We consider the standard two-party communication model. The central problem studied in this article is how much can one save in information complexity by allowing a certain error.
* For arbitrary functions, we obtain lower bounds and upper bounds indicating a gain that is of order Omega(h(epsilon)) and O(h(sqrt{epsilon})). Here h denotes the binary entropy function.
* We analyze the case of the two-bit AND function in detail to show that for this function the gain is Theta(h(epsilon)). This answers a question of Braverman et al. [Braverman, STOC 2013].
* We obtain sharp bounds for the set disjointness function of order n. For the case of the distributional error, we introduce a new protocol that achieves a gain of Theta(sqrt{h(epsilon)}) provided that n is sufficiently large. We apply these results to answer another of question of Braverman et al. regarding the randomized communication complexity of the set disjointness function.
* Answering a question of Braverman [Braverman, STOC 2012], we apply our analysis of the set disjointness function to establish a gap between the two different notions of the prior-free information cost. In light of [Braverman, STOC 2012], this implies that amortized randomized communication complexity is not necessarily equal to the amortized distributional communication complexity with respect to the hardest distribution.
As a consequence, we show that the epsilon-error randomized communication complexity of the set disjointness function of order n is n[C_{DISJ} - Theta(h(epsilon))] + o(n), where C_{DISJ} ~ 0.4827$ is the constant found by Braverman et al. [Braverman, STOC 2012]
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