论文标题
近似量子电路切割
Approximate Quantum Circuit Cutting
论文作者
论文摘要
当前和迫在眉睫的量子硬件缺乏噪声和量子计数有限的可靠性和适用性。量子电路切割 - 一种将大量子电路划分为较小的子电路的技术，其大小适合手头有限的量子资源 - 用于减轻这些问题。但是，与切割相关的经典后处理通常会随着切割和量子计数的数量而成倍增长。本文介绍了近似电路重建的概念。使用基于采样的方法，例如马尔可夫链蒙特卡洛（MCMC），我们概率地选择了重建后高概率的位字符串。重建完整的概率分布时，这避免了过多的计算。我们的结果表明，这种基于抽样的后处理方法具有在NISQ时代及以后的快速和可靠电路重建的巨大潜力。
Current and imminent quantum hardware lacks reliability and applicability due to noise and limited qubit counts. Quantum circuit cutting -- a technique dividing large quantum circuits into smaller subcircuits with sizes appropriate for the limited quantum resource at hand -- is used to mitigate these problems. However, classical postprocessing involved in circuit cutting generally grows exponentially with the number of cuts and quantum counts. This article introduces the notion of approximate circuit reconstruction. Using a sampling-based method like Markov Chain Monte Carlo (MCMC), we probabilistically select bit strings of high probability upon reconstruction. This avoids excessive calculations when reconstructing the full probability distribution. Our results show that such a sampling-based postprocessing method holds great potential for fast and reliable circuit reconstruction in the NISQ era and beyond.
