Maximum-Likelihood Channel Decoding with Quantum Annealing Machine

Autor:	Ide, Naoki, Asayama, Tetsuya, Ueno, Hiroshi, Ohzeki, Masayuki
Rok vydání:	2020
Předmět:	Computer Science - Information Theory
Druh dokumentu:	Working Paper
Popis:	We formulate maximum likelihood (ML) channel decoding as a quadratic unconstraint binary optimization (QUBO) and simulate the decoding by the current commercial quantum annealing machine, D-Wave 2000Q. We prepared two implementations with Ising model formulations, generated from the generator matrix and the parity-check matrix respectively. We evaluated these implementations of ML decoding for low-density parity-check (LDPC) codes, analyzing the number of spins and connections and comparing the decoding performance with belief propagation (BP) decoding and brute-force ML decoding with classical computers. The results show that these implementations are superior to BP decoding in relatively short length codes, and while the performance in the long length codes deteriorates, the implementation from the parity-check matrix formulation still works up to 1k length with fewer spins and connections than that of the generator matrix formulation due to the sparseness of parity-check matrices of LDPC. Comment: accepted at ISITA 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.08689 Zobrazit plný text záznamu View this record from Arxiv