291 research outputs found
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
We consider the weighted belief-propagation (WBP) decoder recently proposed
by Nachmani et al. where different weights are introduced for each Tanner graph
edge and optimized using machine learning techniques. Our focus is on
simple-scaling models that use the same weights across certain edges to reduce
the storage and computational burden. The main contribution is to show that
simple scaling with few parameters often achieves the same gain as the full
parameterization. Moreover, several training improvements for WBP are proposed.
For example, it is shown that minimizing average binary cross-entropy is
suboptimal in general in terms of bit error rate (BER) and a new "soft-BER"
loss is proposed which can lead to better performance. We also investigate
parameter adapter networks (PANs) that learn the relation between the
signal-to-noise ratio and the WBP parameters. As an example, for the (32,16)
Reed-Muller code with a highly redundant parity-check matrix, training a PAN
with soft-BER loss gives near-maximum-likelihood performance assuming simple
scaling with only three parameters.Comment: 5 pages, 5 figures, submitted to ISIT 201
Mathematical Programming Decoding of Binary Linear Codes: Theory and Algorithms
Mathematical programming is a branch of applied mathematics and has recently
been used to derive new decoding approaches, challenging established but often
heuristic algorithms based on iterative message passing. Concepts from
mathematical programming used in the context of decoding include linear,
integer, and nonlinear programming, network flows, notions of duality as well
as matroid and polyhedral theory. This survey article reviews and categorizes
decoding methods based on mathematical programming approaches for binary linear
codes over binary-input memoryless symmetric channels.Comment: 17 pages, submitted to the IEEE Transactions on Information Theory.
Published July 201
Fault-tolerant gates via homological product codes
A method for the implementation of a universal set of fault-tolerant logical
gates is presented using homological product codes. In particular, it is shown
that one can fault-tolerantly map between different encoded representations of
a given logical state, enabling the application of different classes of
transversal gates belonging to the underlying quantum codes. This allows for
the circumvention of no-go results pertaining to universal sets of transversal
gates and provides a general scheme for fault-tolerant computation while
keeping the stabilizer generators of the code sparse.Comment: 11 pages, 3 figures. v2 (published version): quantumarticle
documentclass, expanded discussion on the conditions for a fault tolerance
threshol
Numerical Techniques for Finding the Distances of Quantum Codes
We survey the existing techniques for calculating code distances of classical
codes and apply these techniques to generic quantum codes. For classical and
quantum LDPC codes, we also present a new linked-cluster technique. It reduces
complexity exponent of all existing deterministic techniques designed for codes
with small relative distances (which include all known families of quantum LDPC
codes), and also surpasses the probabilistic technique for sufficiently high
code rates.Comment: 5 pages, 1 figure, to appear in Proceedings of ISIT 2014 - IEEE
International Symposium on Information Theory, Honolul
- …