We show how to efficiently decompose a parameterized multi-qubit Pauli (PMQP) gate into native parameterized two-qubit Pauli (P2QP) gates minimizing both the circuit depth and the number of P2QP gates. Given a realistic quantum computational model, we argue that the technique is optimal in terms of the number of hardware native gates and the overall depth of the decomposition. Starting from PMQP gate decompositions for the path and star hardware graph, we generalize the procedure to any generic hardware graph and provide exact expressions for the depth and number of P2QP gates of the decomposition. Furthermore, we show how to efficiently combine the decomposition of multiple PMQP gates to further reduce the depth as well as the number of P2QP gates for a combinatorial optimization problem using the Lechner–Hauke–Zoller mapping.

中文翻译：

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参数化多量子位泡利门的最佳硬件本机分解


我们展示了如何有效地将参数化多量子位泡利 (PMQP) 门分解为本机参数化双量子位泡利 (P2QP) 门，从而最大限度地减少电路深度和 P2QP 门的数量。给定一个现实的量子计算模型，我们认为该技术在硬件本机门的数量和分解的整体深度方面是最佳的。从路径和星形硬件图的 PMQP 门分解开始，我们将该过程推广到任何通用硬件图，并提供分解的 P2QP 门的深度和数量的精确表达式。此外，我们展示了如何使用 Lechner-Hauke-Zoller 映射有效地组合多个 PMQP 门的分解，以进一步减少组合优化问题的深度和 P2QP 门的数量。