Performance of surface codes in realistic quantum hardware
Performance of surface codes in realistic quantum hardware
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DOI：
10.1103/physreva.106.062428
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发表时间：
2022-12-21
影响因子：
2.9
通讯作者：
Garcia-Frias, Javier
中科院分区：
文献类型：
--
作者：
de Marti Iolius, Antonio;Martinez, Josu Etxezarreta;Garcia-Frias, Javier
表面码的研究通常基于这样的假设：构成表面码晶格的每个量子位都受到独立且同分布 (i.i.d.) 的噪声的影响。然而，最先进的量子处理器的组成量子位的个体弛豫（T1）和相移（T2）时间的真实基准最近表明，每个特定量子位所遭受的退相干效应实际上在强度上有所不同。因此，在本文中，我们引入了独立非同分布（i.n.i.d.）噪声模型，这是一种解释量子位退相干参数的非均匀行为的退相干模型。此外，我们使用 i.n.i.d。模型来研究它如何影响特定系列量子纠错码（称为平面码）的性能。为此，我们使用来自四个最先进的超导处理器的数据：ibmq_brooklyn、ibm_washington、Zuchongzhi 和 Rigetti Aspen-M-1。我们的结果表明 i.i.d.噪声假设高估了表面代码的性能，当表面代码受到 i.n.i.d. 影响时，就代码伪阈值而言，性能可能会下降高达 95%。噪声模型。此外，我们考虑并描述了两种增强 i.n.i.d 下平面码性能的方法。噪音。第一种方法涉及传统最小权重完美匹配（MWPM）解码器的所谓重新加权过程，而第二种方法则利用代码性能与表面代码晶格中量子位排列之间存在的关系。通过结合前两种方法得出的最佳量子位配置可以产生比 i.n.i.d 下的传统 MWPM 解码器高出 650% 的平面码伪阈值。噪音。
Surface codes are generally studied based on the assumption that each of the qubits that make up the surface code lattice suffers noise that is independent and identically distributed (i.i.d.). However, real benchmarks of the individual relaxation (T1) and dephasing (T2) times of the constituent qubits of state-of-the-art quantum processors have recently shown that the decoherence effects suffered by each particular qubit actually vary in intensity. In consequence, in this paper we introduce the independent nonidentically distributed (i.n.i.d.) noise model, a decoherence model that accounts for the nonuniform behavior of the decoherence parameters of qubits. Additionally, we use the i.n.i.d. model to study how it affects the performance of a specific family of quantum error correction codes known as planar codes. For this purpose we employ data from four state-of-the-art superconducting processors: ibmq_brooklyn, ibm_washington, Zuchongzhi, and Rigetti Aspen-M-1. Our results show that the i.i.d. noise assumption overestimates the performance of surface codes, which can suffer up to 95% performance decrements in terms of the code pseudothreshold when they are subjected to the i.n.i.d. noise model. Furthermore, we consider and describe two methods which enhance the performance of planar codes under i.n.i.d. noise. The first method involves a so-called reweighting process of the conventional minimum weight perfect matching (MWPM) decoder, while the second one exploits the relationship that exists between code performance and qubit arrangement in the surface code lattice. The optimum qubit configuration derived through the combination of the previous two methods can yield planar code pseudothreshold values that are up to 650% higher than for the traditional MWPM decoder under i.n.i.d. noise.