针对高斯量子密钥分发的数据协调问题,对高斯连续变量进行了最优量化,实现了Alice和Bob之间的互信息量最大。在分层错误校正(SEC)协议和多电平编码/多级解码(MLC/MSD)协议的基础上,各级码流采用了低密度奇偶校验码(LDPC)进行错误校正,并推出了一次硬信息级间迭代更新公式参与MSD译码算法。算法实现中使用双向十字链表方式存贮LDPC码的稀疏矩阵H,并用C语言实现整个数据协调过程,极大地降低了空间复杂度,提高了协调速度。实验仿真结果表明该算法可在信道信噪比4.9dB以上实现2×105个连续变量序列的可靠协调,协调效率达91.71%,在2.4GHz CPU,32G内存服务器平台上的协调速度可达7262bit/s。 Aiming at the problem of data coordination of Gaussian quantum key distribution, Gaussian continuous variables are optimally quantized, and the mutual information between Alice and Bob is maximized. Based on Hierarchical Error Correction (SEC) protocol and MLC / MSD protocol, LDPC codes are used for error correction at all levels, A hard information iteration update formula to participate in the MSD decoding algorithm. In the algorithm, a two-way cross-linked list is used to store the sparse matrix H of LDPC codes, and the entire data coordination process is realized by using C language, which greatly reduces the space complexity and improves the coordination speed. The experimental results show that the algorithm can achieve the reliable coordination of 2 × 105 continuous variable sequences with the signal to noise ratio of 4.9dB or above, the coordination efficiency reaches 91.71%, and the coordination speed can reach 7262bit / s on the 2.4GHz CPU and 32G memory server platforms. s.