4 research outputs found
New bounds on classical and quantum one-way communication complexity
In this paper we provide new bounds on classical and quantum distributional
communication complexity in the two-party, one-way model of communication. In
the classical model, our bound extends the well known upper bound of Kremer,
Nisan and Ron to include non-product distributions. We show that for a boolean
function f:X x Y -> {0,1} and a non-product distribution mu on X x Y and
epsilon in (0,1/2) constant: D_{epsilon}^{1, mu}(f)= O((I(X:Y)+1) vc(f)), where
D_{epsilon}^{1, mu}(f) represents the one-way distributional communication
complexity of f with error at most epsilon under mu; vc(f) represents the
Vapnik-Chervonenkis dimension of f and I(X:Y) represents the mutual
information, under mu, between the random inputs of the two parties. For a
non-boolean function f:X x Y ->[k], we show a similar upper bound on
D_{epsilon}^{1, mu}(f) in terms of k, I(X:Y) and the pseudo-dimension of f' =
f/k. In the quantum one-way model we provide a lower bound on the
distributional communication complexity, under product distributions, of a
function f, in terms the well studied complexity measure of f referred to as
the rectangle bound or the corruption bound of f . We show for a non-boolean
total function f : X x Y -> Z and a product distribution mu on XxY,
Q_{epsilon^3/8}^{1, mu}(f) = Omega(rec_ epsilon^{1, mu}(f)), where
Q_{epsilon^3/8}^{1, mu}(f) represents the quantum one-way distributional
communication complexity of f with error at most epsilon^3/8 under mu and rec_
epsilon^{1, mu}(f) represents the one-way rectangle bound of f with error at
most epsilon under mu . Similarly for a non-boolean partial function f:XxY -> Z
U {*} and a product distribution mu on X x Y, we show, Q_{epsilon^6/(2 x
15^4)}^{1, mu}(f) = Omega(rec_ epsilon^{1, mu}(f)).Comment: ver 1, 19 page
Optimal Quantum Sample Complexity of Learning Algorithms
In learning theory, the VC dimension of a
concept class is the most common way to measure its "richness." In the PAC
model \Theta\Big(\frac{d}{\eps} + \frac{\log(1/\delta)}{\eps}\Big)
examples are necessary and sufficient for a learner to output, with probability
, a hypothesis that is \eps-close to the target concept . In
the related agnostic model, where the samples need not come from a , we
know that \Theta\Big(\frac{d}{\eps^2} + \frac{\log(1/\delta)}{\eps^2}\Big)
examples are necessary and sufficient to output an hypothesis whose
error is at most \eps worse than the best concept in .
Here we analyze quantum sample complexity, where each example is a coherent
quantum state. This model was introduced by Bshouty and Jackson, who showed
that quantum examples are more powerful than classical examples in some
fixed-distribution settings. However, Atici and Servedio, improved by Zhang,
showed that in the PAC setting, quantum examples cannot be much more powerful:
the required number of quantum examples is
\Omega\Big(\frac{d^{1-\eta}}{\eps} + d + \frac{\log(1/\delta)}{\eps}\Big)\mbox{
for all }\eta> 0. Our main result is that quantum and classical sample
complexity are in fact equal up to constant factors in both the PAC and
agnostic models. We give two approaches. The first is a fairly simple
information-theoretic argument that yields the above two classical bounds and
yields the same bounds for quantum sample complexity up to a \log(d/\eps)
factor. We then give a second approach that avoids the log-factor loss, based
on analyzing the behavior of the "Pretty Good Measurement" on the quantum state
identification problems that correspond to learning. This shows classical and
quantum sample complexity are equal up to constant factors.Comment: 31 pages LaTeX. Arxiv abstract shortened to fit in their
1920-character limit. Version 3: many small changes, no change in result
Correlation in Hard Distributions in Communication Complexity
We study the effect that the amount of correlation in a bipartite
distribution has on the communication complexity of a problem under that
distribution. We introduce a new family of complexity measures that
interpolates between the two previously studied extreme cases: the (standard)
randomised communication complexity and the case of distributional complexity
under product distributions.
We give a tight characterisation of the randomised complexity of Disjointness
under distributions with mutual information , showing that it is
for all . This smoothly interpolates
between the lower bounds of Babai, Frankl and Simon for the product
distribution case (), and the bound of Razborov for the randomised case.
The upper bounds improve and generalise what was known for product
distributions, and imply that any tight bound for Disjointness needs
bits of mutual information in the corresponding distribution.
We study the same question in the distributional quantum setting, and show a
lower bound of , and an upper bound, matching up to a
logarithmic factor.
We show that there are total Boolean functions on inputs that have
distributional communication complexity under all distributions of
information up to , while the (interactive) distributional complexity
maximised over all distributions is for .
We show that in the setting of one-way communication under product
distributions, the dependence of communication cost on the allowed error
is multiplicative in -- the previous upper bounds
had the dependence of more than
Optimal quantum sample complexity of learning algorithms
In learning theory, the VC dimension of a concept class C is the most common way to measure its “richness.” A fundamental result says that the number of examples needed to learn an unknown target concept c∈C under an unknown distribution D, is tightly determined by the VC dimension d of the concept class C. Specifically, in the PAC model
Θ(dϵ+log(1/δ)ϵ)
examples are necessary and sufficient for a learner to output, with probability 1−δ, a hypothesis h that is ϵ-close to the target concept c (measured under D). In the related agnostic model, where the samples need not come from a c∈C, we know that
Θ(dϵ2+log(1/δ)ϵ2)
examples are necessary and sufficient to output an hypothesis h∈C whose error is at most ϵ worse than the error of the best concept in C. Here we analyze quantum sample complexity, where each example is a coherent quantum state. This model was introduced by Bshouty and Jackson (1999), who showed that quantum examples are more powerful than classical examples in some fixed-distribution settings. However, Atıcı and Servedio (2005), improved by Zhang (2010), showed that in the PAC setting (where the learner has to succeed for every distribution), quantum examples cannot be much more powerful: the required number of quantum examples is
Ω(d1−ηϵ+d+log(1/δ)ϵ) for arbitrarily small constant η>0.
Our main result is that quantum and classical sample complexity are in fact equal up to constant factors in both the PAC and agnostic models. We give two proof approaches. The first is a fairly simple information-theoretic argument that yields the above two classical bounds and yields the same bounds for quantum sample complexity up to a log(d/ϵ) factor. We then give a second approach that avoids the log-factor loss, based on analyzing the behavior of the “Pretty Good Measurement” on the quantum state-identification problems that correspond to learning. This shows classical and quantum sample complexity are equal up to constant factors for every concept class C