Predicting Good Quantum Circuit Compilation Options

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).

11 pages, 6 figures, minor changes, to be published at IEEE International Conference on Quantum Software (QSW), 2023

References in corpus (8)

• A Survey on the Explainability of Supervised Machine Learning
• Experimental Comparison of Two Quantum Computing Architectures
• Resource-Aware Quantum Programming with General Recursion and Quantum Control
• MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing
• Assessing requirements to scale to practical quantum advantage
• Compiler Optimization for Quantum Computing Using Reinforcement Learning
• MLGO: a Machine Learning Guided Compiler Optimizations Framework
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Cited by in corpus (12)

• The MQT Handbook: A Summary of Design Automation Tools and Software for Quantum Computing
• Compiler Optimization for Quantum Computing Using Reinforcement Learning
• Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
• MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing
• Towards an Automated Framework for Realizing Quantum Computing Solutions
• Recommending Solution Paths for Solving Optimization Problems with Quantum Computing
• A Hybrid Classical Quantum Computing Approach to the Satellite Mission Planning Problem
• Error estimation in current noisy quantum computers
• Reducing the Compilation Time of Quantum Circuits Using Pre-Compilation on the Gate Level
• Simulation of open quantum systems on universal quantum computers
• Distributing Quantum Computations, Shot-wise
• Graph Neural Network-Based Predictor for Optimal Quantum Hardware Selection