Predicting Good Quantum Circuit Compilation Options

Understand

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.