预测优良的量子电路编译选项
Predicting Good Quantum Circuit Compilation Options
AI总结：
针对量子电路编译选项选择需专业知识、对终端用户难度大的问题，提出基于监督机器学习的MQTPredictor框架，可自动预测最优编译选项组合，准确率高且大幅缩短编译时间，还可提取显性知识，已开源。
摘要：
量子计算的任何潜在应用，在被编码为量子电路后，都需要经过编译才能在量子计算机上执行。根据优良度度量，确定哪种量子比特技术、哪种设备、哪种编译器以及哪些相应设置最适合所考虑的问题，需要专业知识，而对于来自不同领域、试图利用量子计算为自身服务的终端用户来说，这一任务过于复杂。\n在本研究中，我们将该问题视为一项统计分类任务，并探索利用监督机器学习技术来优化量子电路的编译。基于此，我们提出了一个框架，该框架可在给定量子电路的情况下预测这些选项的最佳组合，从而自动为终端用户做出这些决策。\n实验评估表明，在包含3000个量子电路的原型设置下，所提出的框架取得了颇具前景的结果：对于超过四分之三的未见过的测试电路，该框架能确定最佳编译选项组合。此外，对于超过95%的电路，能确定排名前三的编译选项组合——同时编译时间的中位数减少了一个数量级以上。\n此外，由此产生的方法学不仅能为终端用户提供最佳编译选项的预测，还提供了从机器学习技术中提取显性知识的途径。这种知识有两方面作用：它为机器学习在该领域的进一步应用奠定了基础，同时也让人们能够快速验证机器学习算法是否得到了合理训练。相应的框架和预训练分类器作为慕尼黑量子工具包（MQT）的一部分，已在GitHub上公开提供（https://github.com/cda-tum/MQTPredictor）。
英文摘要：
Any potential application of quantum computing, once encoded as a quantum circuit, needs to be compiled in order to be executed on a quantum computer. Deciding which qubit technology, which device, which compiler, and which corresponding settings are best for the considered problem -- according to a measure of goodness -- requires expert knowledge and is overwhelming for end-users from different domains trying to use quantum computing to their advantage. In this work, we treat the problem as a statistical classification task and explore the utilization of supervised machine learning techniques to optimize the compilation of quantum circuits. Based on that, we propose a framework that, given a quantum circuit, predicts the best combination of these options and, therefore, automatically makes these decisions for end-users. Experimental evaluations show that, considering a prototypical setting with 3000 quantum circuits, the proposed framework yields promising results: for more than three quarters of all unseen test circuits, the best combination of compilation options is determined. Moreover, for more than 95% of the circuits, a combination of compilation options within the top-three is determined -- while the median compilation time is reduced by more than one order of magnitude. Furthermore, the resulting methodology not only provides end-users with a prediction of the best compilation options, but also provides means to extract explicit knowledge from the machine learning technique. This knowledge helps in two ways: it lays the foundation for further applications of machine learning in this domain and, also, allows one to quickly verify whether a machine learning algorithm is reasonably trained. The corresponding framework and the pre-trained classifier are publicly available on GitHub (https://github.com/cda-tum/MQTPredictor) as part of the Munich Quantum Toolkit (MQT).
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