In this paper, we propose an analytic approach to the variable sifting based on weighting of qubits and gates. The proposed scheme allows us to optimally sift gates (multi-control single-target reversible gates) within a linear number of steps of computation and provides, in general, that a smaller number of SWAP gates are required to transform a reversible circuit into an Linear Nearest Neighbor (LNN) model than other competing approaches. The method is analyzed for two different models of implementations; it is verified on the experimental data and results are compared with the state-of-the-art algorithms for the design of LNN circuits.

中文翻译：

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使用解析筛选法优化 LNN 可逆电路

在本文中，我们提出了一种基于量子比特和门权重的变量筛选分析方法。所提出的方案允许我们在线性数量的计算步骤内优化筛选门（多控制单目标可逆门），并且通常需要较少数量的 SWAP 门将可逆电路转换为线性电路。与其他竞争方法相比，最近邻 (LNN) 模型。该方法针对两种不同的实现模型进行了分析；对实验数据进行了验证，并将结果与​​用于 LNN 电路设计的最新算法进行了比较。