量子卷积码的高效ML译码
Efficient ML Decoding for Quantum Convolutional Codes
摘要：
针对一般量子卷积码，提出了一种新的译码算法。新的解码器利用经典编码理论中的有用思想，引入了两项创新，与现有的量子维特比解码器相比，大大降低了解码复杂度。首先，新的解码器使用一个有效的基于线性电路的机制来映射一个伴随子到一个候选向量，而现有的算法依赖于一个非平凡的查找表。第二，新算法被巧妙地设计成使得维特比算法的仅一次运行足以定位最可能的错误模式，而现有算法必须多次运行维特比算法。所提出的算法的效率，使我们能够模拟和提出的第一个性能曲线的一般量子卷积码。
英文摘要：
A novel decoding algorithm is developed for general quantum convolutional codes. Exploiting useful ideas from classical coding theory, the new decoder introduces two innovations that drastically reduce the decoding complexity compared to the existing quantum Viterbi decoder. First, the new decoder uses an efficient linear-circuits-based mechanism to map a syndrome to a candidate vector, whereas the existing algorithm relies on a non-trivial lookup table. Second, the new algorithm is cleverly engineered such that only one run of the Viterbi algorithm suffices to locate the most-likely error pattern, whereas the existing algorithm must run the Viterbi algorithm many times. The efficiency of the proposed algorithm allows us to simulate and present the first performance curve of a general quantum convolutional code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
