论文标题
通过噪声猜测解码来纠正量子误差
Quantum Error Correction via Noise Guessing Decoding
论文作者
论文摘要
量子误差校正代码(QECC)在量子通信和量子计算中起着核心作用。通常将实用的量子误差校正代码(例如稳定器代码)结构为适合特定用途,并呈现刚性的代码长度和代码速率。本文表明,对于任何选择的代码速率足够高时,对于任何选择的代码长度来说,可以构建和解码QECC,以达到有限区块长度的最大性能。最近提出的一种称为Grand(猜测随机添加噪声解码)的经典代码的策略打开了门,以有效地解码经典的随机线性代码(RLC),该代码(RLCS)在有限的区块长度方案的最大速率附近执行。通过使用噪声统计数据,Grand是一个以噪声为中心的经典代码的有效通用解码器,前提是存在简单的代码成员资格测试。这些条件特别适合量子系统,因此本文将这些概念扩展到量子随机线性代码(QRLC),这些概念可以构造,但其解码尚不可行。通过结合QRLC和新提出的量子戒指,这项工作表明可以解码易于适应变化条件的QECC。本文首先要评估达到QRLCS渐近性能所需的最小门数,然后提出了一种使用量子噪声统计数据的量子剂量算法,不仅是为了构建自适应代码成员资格测试,而且还可以有效地实施综合征解码。
Quantum error correction codes (QECCs) play a central role in both quantum communications and quantum computation. Practical quantum error correction codes, such as stabilizer codes, are generally structured to suit a specific use, and present rigid code lengths and code rates. This paper shows that it is possible to both construct and decode QECCs that can attain the maximum performance of the finite blocklength regime, for any chosen code length when the code rate is sufficiently high. A recently proposed strategy for decoding classical codes called GRAND (guessing random additive noise decoding) opened doors to efficiently decode classical random linear codes (RLCs) performing near the maximum rate of the finite blocklength regime. By using noise statistics, GRAND is a noise-centric efficient universal decoder for classical codes, provided that a simple code membership test exists. These conditions are particularly suitable for quantum systems, and therefore the paper extends these concepts to quantum random linear codes (QRLCs), which were known to be possible to construct but whose decoding was not yet feasible. By combining QRLCs and a newly proposed quantum-GRAND, this work shows that it is possible to decode QECCs that are easy to adapt to changing conditions. The paper starts by assessing the minimum number of gates in the coding circuit needed to reach the QRLCs' asymptotic performance, and subsequently proposes a quantum-GRAND algorithm that makes use of quantum noise statistics, not only to build an adaptive code membership test, but also to efficiently implement syndrome decoding.