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Compiling a quantum circuit for specific quantum hardware is a challengingtask. Moreover, current quantum computers have severe hardware limitations. Tomake the most use of the limited resources, the compilation process should beoptimized. To improve currents methods, Reinforcement Learning (RL), atechnique in which an agent interacts with an environment to learn complexpolicies to attain a specific goal, can be used. In this work, we present qgym,a software framework derived from the OpenAI gym, together with environmentsthat are specifically tailored towards quantum compilation. The goal of qgym isto connect the research fields of Artificial Intelligence (AI) with quantumcompilation by abstracting parts of the process that are irrelevant to eitherdomain. It can be used to train and benchmark RL agents and algorithms inhighly customizable environments.

qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation