Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with many compilation steps and numerous possible optimization passes. Despite the similarities, the development of compilers for quantum computing is still in its infancy -- lacking mutual consolidation on the best sequence of passes, compatibility, adaptability, and flexibility. In this work, we take advantage of decades of classical compiler optimization and propose a reinforcement learning framework for developing optimized quantum circuit compilation flows. Through distinct constraints and a unifying interface, the framework supports the combination of techniques from different compilers and optimization tools in a single compilation flow. Experimental evaluations show that the proposed framework -- set up with a selection of compilation passes from IBM's Qiskit and Quantinuum's TKET -- significantly outperforms both individual compilers in 73% of cases regarding the expected fidelity. The framework is available on GitHub (https://github.com/cda-tum/MQTPredictor) as part of the Munich Quantum Toolkit (MQT).

Cambrian-AI Research Sr. Analyst Gary Fritz contributed this blog. AI has been advancing dramatically in the last decade. It is able to solve classes of problems (facial recognition, machine translation, autonomous vehicles, and others) that were not suitably handled by traditional methods. But there are many important problems that cannot be addressed by traditional or AI approaches, either within a reasonable timeframe or, possibly, at all. Some of these problems can be attacked with Quantum Computing when that technology comes out of research and becomes more widely available.