论文标题

量子变异学习用于量子误差校正代码

Quantum variational learning for quantum error-correcting codes

论文作者

Cao, Chenfeng, Zhang, Chao, Wu, Zipeng, Grassl, Markus, Zeng, Bei

论文摘要

量子误差校正被认为是大规模耐断层量子计算的必要条件。在过去的二十年中,已经开发了各种量子错误校正代码(QECC)的结构,从而导致许多良好的代码系列。但是,这些代码中的大多数不适用于近期量子设备。在这里,我们提出VARQEC,这是一种使用硬件有效编码电路搜索量子代码的噪声变分量子算法。成本功能的灵感来自QECC,即Knill-Laflamme条件的最一般和基本要求。给定目标噪声通道(或目标代码参数)和硬件连接图,我们优化了浅层量子电路以准备合格代码的基本状态。原则上,VARQEC可以找到任何误差模型的量子代码,无论是添加剂还是非添加,退化或非分类,纯或不纯化的量子代码。我们已经通过(重新)发现一些对称和不对称的代码来验证其有效性Varqec建议不存在$((7,3,3))_ 2 $代码。此外,我们发现了许多新的通道自适应代码,用于涉及最近邻居相关错误的错误模型。我们的工作一般来说,对QECC的理解提供了新的启示,这也可能有助于通过通道自适应误差校正代码来增强近期设备的性能。

Quantum error correction is believed to be a necessity for large-scale fault-tolerant quantum computation. In the past two decades, various constructions of quantum error-correcting codes (QECCs) have been developed, leading to many good code families. However, the majority of these codes are not suitable for near-term quantum devices. Here we present VarQEC, a noise-resilient variational quantum algorithm to search for quantum codes with a hardware-efficient encoding circuit. The cost functions are inspired by the most general and fundamental requirements of a QECC, the Knill-Laflamme conditions. Given the target noise channel (or the target code parameters) and the hardware connectivity graph, we optimize a shallow variational quantum circuit to prepare the basis states of an eligible code. In principle, VarQEC can find quantum codes for any error model, whether additive or non-additive, degenerate or non-degenerate, pure or impure. We have verified its effectiveness by (re)discovering some symmetric and asymmetric codes, e.g., $((n,2^{n-6},3))_2$ for $n$ from 7 to 14. We also found new $((6,2,3))_2$ and $((7,2,3))_2$ codes that are not equivalent to any stabilizer code, and extensive numerical evidence with VarQEC suggests that a $((7,3,3))_2$ code does not exist. Furthermore, we found many new channel-adaptive codes for error models involving nearest-neighbor correlated errors. Our work sheds new light on the understanding of QECC in general, which may also help to enhance near-term device performance with channel-adaptive error-correcting codes.

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