32,430 research outputs found
Bounds on Information Combining With Quantum Side Information
"Bounds on information combining" are entropic inequalities that determine
how the information (entropy) of a set of random variables can change when
these are combined in certain prescribed ways. Such bounds play an important
role in classical information theory, particularly in coding and Shannon
theory; entropy power inequalities are special instances of them. The arguably
most elementary kind of information combining is the addition of two binary
random variables (a CNOT gate), and the resulting quantities play an important
role in Belief propagation and Polar coding. We investigate this problem in the
setting where quantum side information is available, which has been recognized
as a hard setting for entropy power inequalities.
Our main technical result is a non-trivial, and close to optimal, lower bound
on the combined entropy, which can be seen as an almost optimal "quantum Mrs.
Gerber's Lemma". Our proof uses three main ingredients: (1) a new bound on the
concavity of von Neumann entropy, which is tight in the regime of low pairwise
state fidelities; (2) the quantitative improvement of strong subadditivity due
to Fawzi-Renner, in which we manage to handle the minimization over recovery
maps; (3) recent duality results on classical-quantum-channels due to Renes et
al. We furthermore present conjectures on the optimal lower and upper bounds
under quantum side information, supported by interesting analytical
observations and strong numerical evidence.
We finally apply our bounds to Polar coding for binary-input
classical-quantum channels, and show the following three results: (A) Even
non-stationary channels polarize under the polar transform. (B) The blocklength
required to approach the symmetric capacity scales at most sub-exponentially in
the gap to capacity. (C) Under the aforementioned lower bound conjecture, a
blocklength polynomial in the gap suffices.Comment: 23 pages, 6 figures; v2: small correction
R\'enyi Bounds on Information Combining
Bounds on information combining are entropic inequalities that determine how
the information, or entropy, of a set of random variables can change when they
are combined in certain prescribed ways. Such bounds play an important role in
information theory, particularly in coding and Shannon theory. The arguably
most elementary kind of information combining is the addition of two binary
random variables, i.e. a CNOT gate, and the resulting quantities are
fundamental when investigating belief propagation and polar coding. In this
work we will generalize the concept to R\'enyi entropies. We give optimal
bounds on the conditional R\'enyi entropy after combination, based on a certain
convexity or concavity property and discuss when this property indeed holds.
Since there is no generally agreed upon definition of the conditional R\'enyi
entropy, we consider four different versions from the literature. Finally, we
discuss the application of these bounds to the polarization of R\'enyi
entropies under polar codes.Comment: 14 pages, accepted for presentation at ISIT 202
Entropic bounds on coding for noisy quantum channels
In analogy with its classical counterpart, a noisy quantum channel is
characterized by a loss, a quantity that depends on the channel input and the
quantum operation performed by the channel. The loss reflects the transmission
quality: if the loss is zero, quantum information can be perfectly transmitted
at a rate measured by the quantum source entropy. By using block coding based
on sequences of n entangled symbols, the average loss (defined as the overall
loss of the joint n-symbol channel divided by n, when n tends to infinity) can
be made lower than the loss for a single use of the channel. In this context,
we examine several upper bounds on the rate at which quantum information can be
transmitted reliably via a noisy channel, that is, with an asymptotically
vanishing average loss while the one-symbol loss of the channel is non-zero.
These bounds on the channel capacity rely on the entropic Singleton bound on
quantum error-correcting codes [Phys. Rev. A 56, 1721 (1997)]. Finally, we
analyze the Singleton bounds when the noisy quantum channel is supplemented
with a classical auxiliary channel.Comment: 20 pages RevTeX, 10 Postscript figures. Expanded Section II, added 1
figure, changed title. To appear in Phys. Rev. A (May 98
Entanglement-assisted communication of classical and quantum information
We consider the problem of transmitting classical and quantum information
reliably over an entanglement-assisted quantum channel. Our main result is a
capacity theorem that gives a three-dimensional achievable rate region. Points
in the region are rate triples, consisting of the classical communication rate,
the quantum communication rate, and the entanglement consumption rate of a
particular coding scheme. The crucial protocol in achieving the boundary points
of the capacity region is a protocol that we name the classically-enhanced
father protocol. The classically-enhanced father protocol is more general than
other protocols in the family tree of quantum Shannon theoretic protocols, in
the sense that several previously known quantum protocols are now child
protocols of it. The classically-enhanced father protocol also shows an
improvement over a time-sharing strategy for the case of a qubit dephasing
channel--this result justifies the need for simultaneous coding of classical
and quantum information over an entanglement-assisted quantum channel. Our
capacity theorem is of a multi-letter nature (requiring a limit over many uses
of the channel), but it reduces to a single-letter characterization for at
least three channels: the completely depolarizing channel, the quantum erasure
channel, and the qubit dephasing channel.Comment: 23 pages, 5 figures, 1 table, simplification of capacity region--it
now has the simple interpretation as the unit resource capacity region
translated along the classically-enhanced father trade-off curv
Capacities and Capacity-Achieving Decoders for Various Fingerprinting Games
Combining an information-theoretic approach to fingerprinting with a more
constructive, statistical approach, we derive new results on the fingerprinting
capacities for various informed settings, as well as new log-likelihood
decoders with provable code lengths that asymptotically match these capacities.
The simple decoder built against the interleaving attack is further shown to
achieve the simple capacity for unknown attacks, and is argued to be an
improved version of the recently proposed decoder of Oosterwijk et al. With
this new universal decoder, cut-offs on the bias distribution function can
finally be dismissed.
Besides the application of these results to fingerprinting, a direct
consequence of our results to group testing is that (i) a simple decoder
asymptotically requires a factor 1.44 more tests to find defectives than a
joint decoder, and (ii) the simple decoder presented in this paper provably
achieves this bound.Comment: 13 pages, 2 figure
Asymptotics of Fingerprinting and Group Testing: Capacity-Achieving Log-Likelihood Decoders
We study the large-coalition asymptotics of fingerprinting and group testing,
and derive explicit decoders that provably achieve capacity for many of the
considered models. We do this both for simple decoders (fast but suboptimal)
and for joint decoders (slow but optimal), and both for informed and uninformed
settings.
For fingerprinting, we show that if the pirate strategy is known, the
Neyman-Pearson-based log-likelihood decoders provably achieve capacity,
regardless of the strategy. The decoder built against the interleaving attack
is further shown to be a universal decoder, able to deal with arbitrary attacks
and achieving the uninformed capacity. This universal decoder is shown to be
closely related to the Lagrange-optimized decoder of Oosterwijk et al. and the
empirical mutual information decoder of Moulin. Joint decoders are also
proposed, and we conjecture that these also achieve the corresponding joint
capacities.
For group testing, the simple decoder for the classical model is shown to be
more efficient than the one of Chan et al. and it provably achieves the simple
group testing capacity. For generalizations of this model such as noisy group
testing, the resulting simple decoders also achieve the corresponding simple
capacities.Comment: 14 pages, 2 figure
Re-proving Channel Polarization Theorems: An Extremality and Robustness Analysis
The general subject considered in this thesis is a recently discovered coding
technique, polar coding, which is used to construct a class of error correction
codes with unique properties. In his ground-breaking work, Ar{\i}kan proved
that this class of codes, called polar codes, achieve the symmetric capacity
--- the mutual information evaluated at the uniform input distribution ---of
any stationary binary discrete memoryless channel with low complexity encoders
and decoders requiring in the order of operations in the
block-length . This discovery settled the long standing open problem left by
Shannon of finding low complexity codes achieving the channel capacity.
Polar coding settled an open problem in information theory, yet opened plenty
of challenging problems that need to be addressed. A significant part of this
thesis is dedicated to advancing the knowledge about this technique in two
directions. The first one provides a better understanding of polar coding by
generalizing some of the existing results and discussing their implications,
and the second one studies the robustness of the theory over communication
models introducing various forms of uncertainty or variations into the
probabilistic model of the channel.Comment: Preview of my PhD Thesis, EPFL, Lausanne, 2014. For the full version,
see http://people.epfl.ch/mine.alsan/publication
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