MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing

Abstract

Fueled by recent accomplishments in quantum computing hardware and software, an increasing number of problems from various application domains are being explored as potential use cases for this new technology. Similarly to classical computing, realizing an application on a particular quantum device requires the corresponding (quantum) circuit to be compiled so that it can be executed on the device. With a steadily growing number of available devices—each with their own advantages and disadvantages—and a wide variety of different compilation tools, the number of choices to consider when trying to realize an application is quickly exploding. Due to missing tool support and automation, especially end-users who are not quantum computing experts are easily left unsupported and overwhelmed. In this work, we propose a methodology that allows one to automatically select a suitable quantum device for a particular application and provides an optimized compiler for the selected device. The resulting framework—called the MQT Predictor —not only supports end-users in navigating the vast landscape of choices, it also allows mixing and matching compiler passes from various tools to create optimized compilers that transcend the individual tools. Evaluations of an exemplary framework instantiation based on more than 500 quantum circuits and seven devices have shown that—compared to both Qiskit’s and TKET’s most optimized compilation flows for all devices—the MQT Predictor produces circuits within the top-3 out of 14 baselines in more than \(98\% \) of cases while frequently outperforming any tested combination by up to \(53\% \) when optimizing for expected fidelity . Additionally, the framework is trained and evaluated for critical depth as another figure of merit to showcase its flexibility and generalizability—producing circuits within the top-3 in \(89\% \) of cases while frequently outperforming any tested combination by up to \(400\% \) . MQT Predictor is part of the Munich Quantum Toolkit  (MQT) and publicly available as open-source on GitHub (https://github.com/cda-tum/mqt-predictor) and as an easy-to-use Python package (https://pypi.org/p/mqt.predictor).