评估量子电路优化中优化遍序的影响
Evaluating the Effect of the Order of Optimization Passes in Quantum Circuit Optimization
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中文总结 AI 辅助
本文通过Qiskit编译器系统评估16个优化遍的两两组合，发现优化遍顺序显著影响量子电路优化质量，且原生门集是决定最佳顺序的关键因素。
中文摘要 AI 辅助
量子电路优化对于减轻当前量子硬件中固有的噪声至关重要。量子编译器通常按顺序应用多种优化（也称为“优化遍”）来改进电路。这些遍的执行顺序的影响在很大程度上尚未被探索。本文研究了量子电路编译中优化遍顺序的重要性，具体分析了不同优化方法之间的相互作用并量化了它们的相互影响。使用Qiskit的编译器，我们系统地评估了16个选定优化遍的两两组合，在各种基准电路上测量电路深度和门数。我们的发现表明某些优化遍之间存在依赖性，证明遍的顺序影响优化质量。在某些情况下，较差的执行序列可以通过重复应用遍来纠正，从而使其与最佳序列一样好。多遍序列的实验表明，超过两个优化遍可能相互影响，但这总是与先前发现的两两效应相关。我们观察到，对于给定电路，最初选择最佳的优化遍顺序以获得尽可能好的优化是重要的。实验揭示了一些对选择最佳或至少良好顺序至关重要的因素；其中，最终的优化序列最依赖于原生门集。
英文摘要
Quantum circuit optimization is critical for mitigating the noise inherent in current quantum hardware. Quantum compilers typically sequentially apply multiple optimizations (also called ``optimization passes'') to improve the circuit. The impact of the order in which these passes are executed has yet been largely unexplored. This paper investigates the significance of the order of optimization passes within quantum circuit compilation, specifically analyzing interactions between different optimization methods and quantifying their mutual influences. Using Qiskit's compiler, we systematically evaluate pairwise combinations of 16 selected optimization passes, measuring circuit depth and gate count across various benchmark circuits. Our findings indicate dependencies between certain optimization passes, demonstrating that the order of the passes affects the optimization quality. In some cases, the worse performing sequence can be corrected through repeated pass application. %such that they are as good as the best performing one by extending the sequence. Experiments on multi-pass sequences show that more than two optimization passes may have an impact on each other but that this always links to the previously found pairwise effects. We observe that it is important to initially choose the best order of optimization passes to get the best possible optimization for the given circuit. The experiments reveal some factors which are important to choose the best, or at least a good, order; among those, the resulting optimization sequence depends the most on the native gate set.
发表机构
- Ludwig-Maximilians-Universität in Munich（慕尼黑路德维希-马克西米利安大学）
- Technical University of Munich（慕尼黑工业大学）
- Leibniz Supercomputing Centre（莱布尼茨超级计算中心）
机构由 AI 辅助整理，请以论文原文为准。
