qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation
This work addresses the problem of optimizing quantum compilation for researchers in quantum computing and AI, though it is incremental as it adapts existing RL frameworks to a new domain.
The authors tackled the challenge of quantum circuit compilation for limited hardware by introducing qgym, a software framework based on OpenAI Gym, which resulted in a customizable environment for training and benchmarking reinforcement learning agents in quantum compilation tasks.
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.