大规模量子计算的性能与资源综合分析
Integrated Analysis of Performance and Resource of Large-Scale Quantum Computing
摘要：
要了解大规模量子计算的可行性，需要准确分析量子计算的性能和量子资源。然而，迄今为止报告的大多数分析都集中在统计检查上，即，简单地基于单个数据计算性能和资源，更糟糕的是通常只考虑了几个组件。在这项工作中，为了实现更精确的分析，我们提出了一个实际的量子计算模型的三个组件（\textit{algorithm}，\textit{error correction}和\textit{device}）在一个现实的量子计算机系统架构的综合分析方法。为了实现上述方法，我们开发了一个量子计算框架，该框架由编译、系统和构建块三个功能层组成。该框架首次实现了量子算法从物理量子比特级到系统结构级的映射，并给出了相应的容错方案。因此，该方法可以测量量子计算机实际运行时的动态情况的影响。通过使用我们的方法，我们发现Shor算法分解512位整数需要$8.78\times 10^ 5$小时。我们还展示了如何使用所提出的方法来分析最佳级联水平和代码距离的容错量子计算。
英文摘要：
To see the feasibility of a large-scale quantum computing, it is required to accurately analyze the performance and the quantum resource. However, most of the analysis reported so far have focused on the statistical examination, i.e., simply calculating the performance and resource based on individual data, and even worse usually only a few components have been considered. In this work, to achieve more exact analysis, we propose an integrated analysis method for a practical quantum computing model with three components (\textit{algorithm}, \textit{error correction} and \textit{device}) under a realistic quantum computer system architecture. To implement the above method, we develop a quantum computing framework composed of three functional layers: compile, system and building block. This framework can support, for the first time, the mapping of quantum algorithm from physical qubit level to system architecture level with a given fault-tolerant scheme. Therefore, the proposed method can measure the effect of dynamic situation when the quantum computer practically runs. By using our method, we found that Shor algorithm to factorize 512-bit integer requires $8.78\times 10^ 5$ hours. We also show how the proposed method can be used for analyzing optimal concatenation level and code distance of fault-tolerant quantum computing.
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。
