qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation
Stan van der Linde Willem de Kok Tariq Bontekoe Sebastian Feld
Compiling a quantum circuit for specific quantum hardware is a challenging
task. Moreover, current quantum computers have severe hardware limitations. To
make the most use of the limited resources, the compilation process should be
optimized. To improve currents methods, Reinforcement Learning (RL), a
technique in which an agent interacts with an environment to learn complex
policies to attain a specific goal, can be used. In this work, we present qgym,
a software framework derived from the OpenAI gym, together with environments
that are specifically tailored towards quantum compilation. The goal of qgym is
to connect the research fields of Artificial Intelligence (AI) with quantum
compilation by abstracting parts of the process that are irrelevant to either
domain. It can be used to train and benchmark RL agents and algorithms in
highly customizable environments.