17,096 research outputs found
On the communication complexity of XOR functions
An XOR function is a function of the form g(x,y) = f(x + y), for some boolean
function f on n bits. We study the quantum and classical communication
complexity of XOR functions. In the case of exact protocols, we completely
characterise one-way communication complexity for all f. We also show that,
when f is monotone, g's quantum and classical complexities are quadratically
related, and that when f is a linear threshold function, g's quantum complexity
is Theta(n). More generally, we make a structural conjecture about the Fourier
spectra of boolean functions which, if true, would imply that the quantum and
classical exact communication complexities of all XOR functions are
asymptotically equivalent. We give two randomised classical protocols for
general XOR functions which are efficient for certain functions, and a third
protocol for linear threshold functions with high margin. These protocols
operate in the symmetric message passing model with shared randomness.Comment: 18 pages; v2: minor correction
One-way communication complexity and non-adaptive decision trees
We study the relationship between various one-way communication complexity measures of a composed function with the analogous decision tree complexity of the outer function. We consider two gadgets: the AND function on 2 inputs, and the Inner Product on a constant number of inputs. More generally, we show the following when the gadget is Inner Product on 2b input bits for all b ≥ 2, denoted IP. If f is a total Boolean function that depends on all of its n input bits, then the bounded-error one-way quantum communication complexity of f ◦ IP equals Ω(n(b - 1)). If f is a partial Boolean function, then the deterministic one-way communication complexity of f ◦ IP is at least Ω(b · D→dt (f)), where D→dt (f) denotes non-adaptive decision tree complexity of f. To prove our quantum lower bound, we first show a lower bound on the VC-dimension of f ◦ IP. We then appeal to a result of Klauck [STOC’00], which immediately yields our quantum lower bound. Our deterministic lower bound relies on a combinatorial result independently proven by Ahlswede and Khachatrian [Adv. Appl. Math.’98], and Frankl and Tokushige [Comb.’99]. It is known due to a result of Montanaro and Osborne [arXiv’09] that the deterministic one-way communication complexity of f ◦ XOR equals the non-adaptive parity decision tree complexity of f. In contrast, we show the following when the inner gadget is the AND function on 2 input bits. There exists a function for which even the quantum non-adaptive AND decision tree complexity of f is exponentially large in the deterministic one-way communication complexity of f ◦ AND. However, for symmetric functions f, the non-adaptive AND decision tree complexity of f is at most quadratic in the (even two-way) communication complexity of f ◦ AND. In view of the first bullet, a lower bound on non-adaptive AND decision tree complexity of f does not lift to a lower bound on one-way communication complexity of f ◦ AND. The proof of the first bullet above uses the well-studied Odd-Max-Bit function. For the second bullet, we first observe a connection between the one-way communication complexity of f and the Möbius sparsity of f, and then give a lower bound on the Möbius sparsity of symmetric functions. An upper bound on the non-adaptive AND decision tree complexity of symmetric functions follows implicitly from prior work on combinatorial group testing; for the sake of completeness, we include a proof of this result. It is well known that the rank of the communication matrix of a function F is an upper bound on its deterministic one-way communication complexity. This bound is known to be tight for some F. However, in our final result we show that this is not the case when F = f ◦ AND. More precisely we show that for all f, the deterministic one-way communication complexity of F = f ◦ AND is at most (rank(MF))(1 - Ω(1)), where MF denotes the communication matrix of F
New Bounds for the Garden-Hose Model
We show new results about the garden-hose model. Our main results include
improved lower bounds based on non-deterministic communication complexity
(leading to the previously unknown bounds for Inner Product mod 2
and Disjointness), as well as an upper bound for the
Distributed Majority function (previously conjectured to have quadratic
complexity). We show an efficient simulation of formulae made of AND, OR, XOR
gates in the garden-hose model, which implies that lower bounds on the
garden-hose complexity of the order will be
hard to obtain for explicit functions. Furthermore we study a time-bounded
variant of the model, in which even modest savings in time can lead to
exponential lower bounds on the size of garden-hose protocols.Comment: In FSTTCS 201
Fourier sparsity, spectral norm, and the Log-rank conjecture
We study Boolean functions with sparse Fourier coefficients or small spectral
norm, and show their applications to the Log-rank Conjecture for XOR functions
f(x\oplus y) --- a fairly large class of functions including well studied ones
such as Equality and Hamming Distance. The rank of the communication matrix M_f
for such functions is exactly the Fourier sparsity of f. Let d be the F2-degree
of f and D^CC(f) stand for the deterministic communication complexity for
f(x\oplus y). We show that 1. D^CC(f) = O(2^{d^2/2} log^{d-2} ||\hat f||_1). In
particular, the Log-rank conjecture holds for XOR functions with constant
F2-degree. 2. D^CC(f) = O(d ||\hat f||_1) = O(\sqrt{rank(M_f)}\logrank(M_f)).
We obtain our results through a degree-reduction protocol based on a variant of
polynomial rank, and actually conjecture that its communication cost is already
\log^{O(1)}rank(M_f). The above bounds also hold for the parity decision tree
complexity of f, a measure that is no less than the communication complexity
(up to a factor of 2).
Along the way we also show several structural results about Boolean functions
with small F2-degree or small spectral norm, which could be of independent
interest. For functions f with constant F2-degree: 1) f can be written as the
summation of quasi-polynomially many indicator functions of subspaces with
\pm-signs, improving the previous doubly exponential upper bound by Green and
Sanders; 2) being sparse in Fourier domain is polynomially equivalent to having
a small parity decision tree complexity; 3) f depends only on polylog||\hat
f||_1 linear functions of input variables. For functions f with small spectral
norm: 1) there is an affine subspace with co-dimension O(||\hat f||_1) on which
f is a constant; 2) there is a parity decision tree with depth O(||\hat f||_1
log ||\hat f||_0).Comment: v2: Corollary 31 of v1 removed because of a bug in the proof. (Other
results not affected.
Computing on Anonymous Quantum Network
This paper considers distributed computing on an anonymous quantum network, a
network in which no party has a unique identifier and quantum communication and
computation are available. It is proved that the leader election problem can
exactly (i.e., without error in bounded time) be solved with at most the same
complexity up to a constant factor as that of exactly computing symmetric
functions (without intermediate measurements for a distributed and superposed
input), if the number of parties is given to every party. A corollary of this
result is a more efficient quantum leader election algorithm than existing
ones: the new quantum algorithm runs in O(n) rounds with bit complexity
O(mn^2), on an anonymous quantum network with n parties and m communication
links. Another corollary is the first quantum algorithm that exactly computes
any computable Boolean function with round complexity O(n) and with smaller bit
complexity than that of existing classical algorithms in the worst case over
all (computable) Boolean functions and network topologies. More generally, any
n-qubit state can be shared with that complexity on an anonymous quantum
network with n parties.Comment: 25 page
Fundamentals of Large Sensor Networks: Connectivity, Capacity, Clocks and Computation
Sensor networks potentially feature large numbers of nodes that can sense
their environment over time, communicate with each other over a wireless
network, and process information. They differ from data networks in that the
network as a whole may be designed for a specific application. We study the
theoretical foundations of such large scale sensor networks, addressing four
fundamental issues- connectivity, capacity, clocks and function computation.
To begin with, a sensor network must be connected so that information can
indeed be exchanged between nodes. The connectivity graph of an ad-hoc network
is modeled as a random graph and the critical range for asymptotic connectivity
is determined, as well as the critical number of neighbors that a node needs to
connect to. Next, given connectivity, we address the issue of how much data can
be transported over the sensor network. We present fundamental bounds on
capacity under several models, as well as architectural implications for how
wireless communication should be organized.
Temporal information is important both for the applications of sensor
networks as well as their operation.We present fundamental bounds on the
synchronizability of clocks in networks, and also present and analyze
algorithms for clock synchronization. Finally we turn to the issue of gathering
relevant information, that sensor networks are designed to do. One needs to
study optimal strategies for in-network aggregation of data, in order to
reliably compute a composite function of sensor measurements, as well as the
complexity of doing so. We address the issue of how such computation can be
performed efficiently in a sensor network and the algorithms for doing so, for
some classes of functions.Comment: 10 pages, 3 figures, Submitted to the Proceedings of the IEE
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