Compiler Optimization for Quantum Computing Using Reinforcement Learning. (arXiv:2212.04508v1 [quant-ph])

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 over 70% of cases
regarding the expected fidelity. The framework is available on GitHub
(https://github.com/cda-tum/MQTPredictor).