1,493 research outputs found
Multilevel Decoders Surpassing Belief Propagation on the Binary Symmetric Channel
In this paper, we propose a new class of quantized message-passing decoders
for LDPC codes over the BSC. The messages take values (or levels) from a finite
set. The update rules do not mimic belief propagation but instead are derived
using the knowledge of trapping sets. We show that the update rules can be
derived to correct certain error patterns that are uncorrectable by algorithms
such as BP and min-sum. In some cases even with a small message set, these
decoders can guarantee correction of a higher number of errors than BP and
min-sum. We provide particularly good 3-bit decoders for 3-left-regular LDPC
codes. They significantly outperform the BP and min-sum decoders, but more
importantly, they achieve this at only a fraction of the complexity of the BP
and min-sum decoders.Comment: 5 pages, in Proc. of 2010 IEEE International Symposium on Information
Theory (ISIT
Sparse Graph Codes for Quantum Error-Correction
We present sparse graph codes appropriate for use in quantum
error-correction. Quantum error-correcting codes based on sparse graphs are of
interest for three reasons. First, the best codes currently known for classical
channels are based on sparse graphs. Second, sparse graph codes keep the number
of quantum interactions associated with the quantum error correction process
small: a constant number per quantum bit, independent of the blocklength.
Third, sparse graph codes often offer great flexibility with respect to
blocklength and rate. We believe some of the codes we present are unsurpassed
by previously published quantum error-correcting codes.Comment: Version 7.3e: 42 pages. Extended version, Feb 2004. A shortened
version was resubmitted to IEEE Transactions on Information Theory Jan 20,
200
Enhanced Feedback Iterative Decoding of Sparse Quantum Codes
Decoding sparse quantum codes can be accomplished by syndrome-based decoding
using a belief propagation (BP) algorithm.We significantly improve this
decoding scheme by developing a new feedback adjustment strategy for the
standard BP algorithm. In our feedback procedure, we exploit much of the
information from stabilizers, not just the syndrome but also the values of the
frustrated checks on individual qubits of the code and the channel model.
Furthermore we show that our decoding algorithm is superior to belief
propagation algorithms using only the syndrome in the feedback procedure for
all cases of the depolarizing channel. Our algorithm does not increase the
measurement overhead compared to the previous method, as the extra information
comes for free from the requisite stabilizer measurements.Comment: 10 pages, 11 figures, Second version, To be appeared in IEEE
Transactions on Information Theor
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