AI 中文总结
研究二维网格上对易门电路的高效转译问题，提出一种交替构建SWAP层序列和更新量子比特布局的方法，相比标准方法，减少了电路深度和门数量，提升了近似比率，还能进行更多量子比特实验。
AI 中文摘要
组合优化问题在许多应用中至关重要但求解具有挑战性。量子近似优化算法（QAOA）等量子方法提供了处理此类问题的新工具。然而，QAOA电路继承目标哈密顿量的相互作用结构，在编译到连接性有限的硬件上时通常会导致深度电路。因此，高效转译对其实际性能至关重要。本文提出一种针对二维晶格上由对易两比特门块组成的电路的转译方案。与基于随机初始映射和固定路由的标准方法不同，我们的方法在构建依赖问题的SWAP层序列和更新量子比特布局之间交替。通过使路由适应所需相互作用，对于边少的图能产生显著更短的电路。我们在随机正则图上的最大割（MC）和厄多斯 - 雷尼图上的最大独立集（MIS）的QAOA实例上对我们的方法进行基准测试。与标准方法相比，我们将电路深度和门数量减少了约两倍，能够进行多达80个量子比特的实验，并将MC的近似比率提高了6.6%，将MIS的近似比率提高了9.3%。
英文摘要
Combinatorial optimization problems are central to many applications but can be challenging to solve. Quantum approaches such as the Quantum Approximate Optimization Algorithm (QAOA) offer new tools with which to tackle such problems. However, QAOA circuits inherit the interaction structure of the target Hamiltonian, often resulting in deep circuits when compiled onto hardware with limited connectivity. Efficient transpilation is therefore critical to their practical performance. In this work, we propose a transpilation scheme for circuits consisting of blocks of commuting two-qubit gates on two-dimensional lattices. Unlike standard approaches based on random initial mappings and fixed routing, our method alternates between constructing problem-dependent SWAP-layer sequences and updating the qubit layout. By adapting the routing to the required interactions, this yields significantly shorter circuits for graphs with few edges. We benchmark our approach on QAOA instances for Maximum Cut (MC) on Random Regular graphs and Maximum Independent Set (MIS) on Erdős-Rényi graphs. Compared to standard methods, we reduce circuit depth and gate count by about a factor of two, enabling experiments with up to $80$ qubits and improving approximation ratios by up to $6.6\%$ for MC and $9.3\%$ for MIS.
Comments20 pages, 17 figures
