Quantum Low-Density Parity-Check Codes

Quantum error correction is an indispensable ingredient for scalable quantum computing. In this Perspective we discuss a particular class of quantum codes called “quantum low-density parity-check (LDPC) codes.” The codes we discuss are alternatives to the surface code, which is currently the leading candidate to implement quantum fault tolerance. We introduce the zoo of quantum LDPC codes and discuss their potential for making quantum computers robust with regard to noise. In particular, we explain recent advances in the theory of quantum LDPC codes related to certain product constructions and discuss open problems in the field

A Class of Quantum LDPC Codes Constructed From Finite Geometries

Low-density parity check (LDPC) codes are a significant class of classical codes with many applications. Several good LDPC codes have been constructed using random, algebraic, and finite geometries approaches, with containing cycles of length at least six in their Tanner graphs. However, it is impossible to design a self-orthogonal parity check matrix of an LDPC code without introducing cycles of length four. In this paper, a new class of quantum LDPC codes based on lines and points of finite geometries is constructed. The parity check matrices of these codes are adapted to be self-orthogonal with containing only one cycle of length four. Also, the column and row weights, and bounds on the minimum distance of these codes are given. As a consequence, the encoding and decoding algorithms of these codes as well as their performance over various quantum depolarizing channels will be investigated.Comment: 5pages, 2 figure

Adaptive Cut Generation Algorithm for Improved Linear Programming Decoding of Binary Linear Codes

Linear programming (LP) decoding approximates maximum-likelihood (ML) decoding of a linear block code by relaxing the equivalent ML integer programming (IP) problem into a more easily solved LP problem. The LP problem is defined by a set of box constraints together with a set of linear inequalities called "parity inequalities" that are derived from the constraints represented by the rows of a parity-check matrix of the code and can be added iteratively and adaptively. In this paper, we first derive a new necessary condition and a new sufficient condition for a violated parity inequality constraint, or "cut," at a point in the unit hypercube. Then, we propose a new and effective algorithm to generate parity inequalities derived from certain additional redundant parity check (RPC) constraints that can eliminate pseudocodewords produced by the LP decoder, often significantly improving the decoder error-rate performance. The cut-generating algorithm is based upon a specific transformation of an initial parity-check matrix of the linear block code. We also design two variations of the proposed decoder to make it more efficient when it is combined with the new cut-generating algorithm. Simulation results for several low-density parity-check (LDPC) codes demonstrate that the proposed decoding algorithms significantly narrow the performance gap between LP decoding and ML decoding

On Constructing Low-Density Parity-Check Codes

This thesis focuses on designing Low-Density Parity-Check (LDPC) codes for forward-error-correction. The target application is real-time multimedia communications over packet networks. We investigate two code design issues, which are important in the target application scenarios, designing LDPC codes with low decoding latency, and constructing capacity-approaching LDPC codes with very low error probabilities. On designing LDPC codes with low decoding latency, we present a framework for optimizing the code parameters so that the decoding can be fulfilled after only a small number of iterative decoding iterations. The brute force approach for such optimization is numerical intractable, because it involves a difficult discrete optimization programming. In this thesis, we show an asymptotic approximation to the number of decoding iterations. Based on this asymptotic approximation, we propose an approximate optimization framework for finding near-optimal code parameters, so that the number of decoding iterations is minimized. The approximate optimization approach is numerically tractable. Numerical results confirm that the proposed optimization approach has excellent numerical properties, and codes with excellent performance in terms of number of decoding iterations can be obtained. Our results show that the numbers of decoding iterations of the codes by the proposed design approach can be as small as one-fifth of the numbers of decoding iterations of some previously well-known codes. The numerical results also show that the proposed asymptotic approximation is generally tight for even non-extremely limiting cases. On constructing capacity-approaching LDPC codes with very low error probabilities, we propose a new LDPC code construction scheme based on $2$-lifts. Based on stopping set distribution analysis, we propose design criteria for the resulting codes to have very low error floors. High error floors are the main problems of previously constructed capacity-approaching codes, which prevent them from achieving very low error probabilities. Numerical results confirm that codes with very low error floors can be obtained by the proposed code construction scheme and the design criteria. Compared with the codes by the previous standard construction schemes, which have error floors at the levels of $10^{-3}$ to $10^{-4}$, the codes by the proposed approach do not have observable error floors at the levels higher than $10^{-7}$. The error floors of the codes by the proposed approach are also significantly lower compared with the codes by the previous approaches to constructing codes with low error floors

EXTREMAL ABSORBING SETS IN LOW-DENSITY PARITY-CHECK CODES

Absorbing sets are combinatorial structures in the Tanner graphs of low-density parity-check (LDPC) codes that have been shown to inhibit the high signal-to-noise ratio performance of iterative decoders over many communication channels. Absorbing sets of minimum size are the most likely to cause errors, and thus have been the focus of much research. In this paper, we determine the sizes of absorbing sets that can occur in general and left-regular LDPC code graphs, with emphasis on the range of b for a given a for which an (a, b)-absorbing set may exist. We identify certain cases of extremal absorbing sets that are elementary, a particularly harmful class of absorbing sets, and also introduce the notion of minimal absorbing sets which will help in designing absorbing set removal algorithms

Challenges and Some New Directions in Channel Coding

Three areas of ongoing research in channel coding are surveyed, and recent developments are presented in each area: spatially coupled Low-Density Parity-Check (LDPC) codes, nonbinary LDPC codes, and polar coding.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/JCN.2015.00006