论文标题
用量子退火机器解码的最大似然通道解码
Maximum-Likelihood Channel Decoding with Quantum Annealing Machine
论文作者
论文摘要
我们将最大似然（ML）通道解码为二次不强化二进制优化（QUBO），并通过当前的商业量子退火机模拟解码，D-WAVE 2000Q。我们准备了两种实现，分别是由生成器矩阵和奇偶校验检查矩阵生成的ISING模型公式。我们评估了低密度平等检查（LDPC）代码的ML解码实现，分析了旋转和连接的数量，并将解码性能与信念传播（BP）解码和蛮力ML解码进行比较。结果表明，这些实现在相对较短的长度代码中优于BP解码，而长长代码的性能恶化，而来自奇偶校验检查矩阵配方的实现仍然高达1K长度，旋转和连接较少，而旋转和连接的速度却比发电机Matrix配方较少，因为Pare-Matrix配方是由Parice-Check Matcheck Matrices of Ldpc of Ldpc的弹性。
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.
