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arXiv 2212.05071quant-ph
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用于针对泡利噪声的量子编码的低深度随机克利福德电路:基于张量网络解码器

Low-depth random Clifford circuits for quantum coding against Pauli noise using a tensor-network decoder

Andrew S. Darmawan, Yoshifumi Nakata, Shiro Tamiya, Hayata Yamasaki

AI总结:

本研究证明,一维对数深度随机克利福德编码电路在泡利噪声下可达到哈希界编码率,并开发了相应的张量网络最大似然解码算法。

摘要:

近期工作[M. J. Gullans等人,Physical Review X, 11(3):031066 (2021)]表明,由随机克利福德编码电路定义的量子纠错码,即使嵌入一维空间(1D)的n量子比特随机电路具有对数深度d=O(log n),也能在纠错中实现非零编码率。然而,该结论仅针对简单的擦除噪声模型得到验证。在本工作中,我们发现这一期望性质确实适用于常规的泡利噪声模型。具体而言,我们通过数值模拟证明,对于不同强度的去极化噪声,即使在一维中将电路深度限制为d=O(log n),也能达到哈希界,即已知通过d=O(n)深度随机编码电路可达到的编码率。这一分析得益于我们开发的一种张量网络最大似然解码算法,该算法能高效处理一维中的对数深度编码电路。

英文摘要:

Recent work [M. J. Gullans et al., Physical Review X, 11(3):031066 (2021)] has shown that quantum error correcting codes defined by random Clifford encoding circuits can achieve a non-zero encoding rate in correcting errors even if the random circuits on $n$ qubits, embedded in one spatial dimension (1D), have a logarithmic depth $d=\mathcal{O}(\log{n})$. However, this was demonstrated only for a simple erasure noise model. In this work, we discover that this desired property indeed holds for the conventional Pauli noise model. Specifically, we numerically demonstrate that the hashing bound, i.e., a rate known to be achieved with $d=\mathcal{O}(n)$-depth random encoding circuits, can be attained even when the circuit depth is restricted to $d=\mathcal{O}(\log n)$ in 1D for depolarizing noise of various strengths. This analysis is made possible with our development of a tensor-network maximum-likelihood decoding algorithm that works efficiently for $\log$-depth encoding circuits in 1D.

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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