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arXiv 2601.08387cs.ITmath.IT
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一种高效的量子低密度奇偶校验码采样算法

An Efficient Algorithm to Sample Quantum Low-Density Parity-Check Codes

Polytechnic University of Marche(马尔凯理工大学)

Paolo Santini

AI总结:

本文提出了一种高效的量子LDPC码校验矩阵采样算法,利用信息集解码技术实现稀疏自正交矩阵的随机生成。

摘要:

本文提出了一种高效的算法,用于随机生成稀疏矩阵,作为量子低密度奇偶校验(LDPC)码的校验矩阵。为了简化处理,我们主要将我们的算法描述为一种采样双含二进制LDPC码的技术,因此生成一个稀疏矩阵H∈F_2^{r×n},使得H H^T = 0。然而,我们证明该算法可以很容易地推广到非二元有限域上的双含LDPC码以及更一般的量子稳定器LDPC码。虽然已经存在几种构造方法,但所有方法都或多或少具有代数性质,因为它们强制某些特定属性(例如矩阵是准循环的)。相反,我们的算法是纯粹组合的,因为我们除了要求H的行足够稀疏外,不需要其他条件。从这个意义上说,我们可以将我们的算法视为一种采样稀疏、自正交且尽可能随机的矩阵的方法。我们的算法在概念上非常简单,作为关键成分,使用信息集解码(ISD)来逐行采样H的行。ISD的使用是根本性的,因为没有它,高效的采样将不可行。我们对算法进行了理论分析,确定了可以采样的参数范围以及预期的计算复杂度。数值模拟和基准测试证实了我们方法的可行性和效率。

英文摘要:

In this paper, we present an efficient algorithm to sample random sparse matrices to be used as check matrices for quantum Low-Density Parity-Check (LDPC) codes. To ease the treatment, we mainly describe our algorithm as a technique to sample a dual-containing binary LDPC code, hence, a sparse matrix $\mathbf H\in\mathbb F_2^{r\times n}$ such that $\mathbf H\mathbf H^\top = \mathbf 0$. However, as we show, the algorithm can be easily generalized to sample dual-containing LDPC codes over non binary finite fields as well as more general quantum stabilizer LDPC codes. While several constructions already exist, all of them are somewhat algebraic as they impose some specific property (e.g., the matrix being quasi-cyclic). Instead, our algorithm is purely combinatorial as we do not require anything apart from the rows of $\mathbf H$ being sparse enough. In this sense, we can think of our algorithm as a way to sample sparse, self-orthogonal matrices that are as random as possible. Our algorithm is conceptually very simple and, as a key ingredient, uses Information Set Decoding (ISD) to sample the rows of $\mathbf H$, one at a time. The use of ISD is fundamental as, without it, efficient sampling would not be feasible. We give a theoretical characterization of our algorithm, determining which ranges of parameters can be sampled as well as the expected computational complexity. Numerical simulations and benchmarks confirm the feasibility and efficiency of our approach.

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。

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