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

TL;DR

qgym is a customizable framework that combines AI and quantum compilation, enabling training and benchmarking of RL agents to optimize quantum circuit compilation for hardware limitations.

Contribution

The paper introduces qgym, a novel software framework derived from OpenAI gym, tailored for quantum compilation tasks and RL benchmarking.

Findings

Facilitates training of RL agents for quantum compilation

Provides customizable environments for benchmarking algorithms

Bridges AI and quantum hardware optimization

Abstract

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.