Predicting Good Quantum Circuit Compilation Options

• URL: http://arxiv.org/abs/2210.08027v3
• Date: Fri, 19 May 2023 11:12:32 GMT
• Title: Predicting Good Quantum Circuit Compilation Options
• Authors: Nils Quetschlich, Lukas Burgholzer, Robert Wille
• Abstract summary: We propose a framework that predicts the best combination of compilation options for quantum circuits. For more than 95% of the circuits, a combination of compilation options within the top-three is determined. The resulting methodology lays the foundation for further applications of machine learning in this domain.
• Score: 3.610459670994051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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