发表机构
Karlsruhe Institute of Technology; Technical University of Munich（卡尔斯鲁厄理工学院; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对量子电路编译优化的固定默认配置不足问题，结合监督机器学习与自动调优，构建Qiskit编译器的预测模型，可自动选通道减少两量子比特门，效果优于Qiskit默认优化。
AI 中文摘要
量子软件工程是一个新兴研究领域，专注于将量子编程范式高效嵌入现有软件生态系统。该领域的一个关键方面是使用基于门的编程实现量子算法，随后对生成的量子电路进行底层优化，这一过程通常由所谓的编译流水线完成。这些流水线中的一个重大挑战是确定对给定电路应用哪些优化。这一决策通常基于固定的默认配置，该配置统一应用于所有电路，常常错失更激进优化电路的机会。在这项工作中，我们通过将自动调优与监督机器学习相结合，开发了一种用于选择编译器通道的自动化方法，以解决这一挑战。为训练我们的机器学习模型，我们采用基于特征模型的采样生成代表性数据集，该数据集研究来自最先进基准套件MQT Bench的数千个电路上不同Qiskit编译器通道组合的性能。利用这些数据，我们为Qiskit编译流水线构建了一个预测模型扩展，该扩展使用机器学习模型自动选择编译器通道组合，旨在最大程度减少两量子比特门数量。我们的实证评估表明，我们模型选择的组合从未被Qiskit的优化级别超越，平均实现了额外19.1%至32.4%的两量子比特门减少，且在Qiskit完全无法实现减少的某些电路中，我们的方法能实现高达95.8%的减少。
英文摘要
Quantum software engineering is an emerging research field focusing on efficiently embedding the quantum programming paradigm into existing software ecosystems. A key aspect of this field is the realization of quantum algorithms using gate-based programming and the subsequent low-level optimization of the resulting quantum circuits, a process that is commonly performed by so-called transpilation pipelines. One significant challenge in these pipelines is determining which optimizations to apply to a given circuit. This decision is usually based on fixed default configurations that are uniformly applied to all circuits, frequently resulting in missed opportunities for more aggressive circuit optimization. In this work, we tackle this challenge by applying autotuning with supervised machine learning to develop an automated method for selection of transpiler passes. To train our machine-learning models, we employ feature-model based sampling to generate a representative dataset that examines how different combinations of Qiskit transpiler passes perform across thousands of circuits drawn from the state-of-the-art benchmarking suite MQT Bench. Using these data, we build a predictive model extension for the Qiskit transpilation pipeline that uses a machine learning model to automatically select combinations of transpiler passes aiming to achieve a maximum reduction in two-qubit gates. Our empirical evaluation shows that the combinations selected by our model are never outperformed by Qiskit's optimization levels, achieve on average an additional 19.1$\%$ - 32.4$\%$ reduction in two-qubit gates, and for some circuits finds reductions of up to $95.8\%$ in cases where Qiskit achieves no reduction at all.
Comments30 pages, 9 figures, preprint
