Challenges for Reinforcement Learning in Quantum Circuit Design
This work addresses the challenge of optimizing quantum circuit architectures in the NISQ era, which is incremental as it applies existing RL methods to a specific domain.
The paper tackles the problem of improving quantum circuit design by leveraging reinforcement learning, proposing a formal framework and benchmark comparisons to evaluate current RL algorithms.
Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. This work considers the latter, leveraging reinforcement learning (RL) to improve quantum circuit design (QCD), which we formalize by a set of generic objectives. Furthermore, we propose qcd-gym, a concrete framework formalized as a Markov decision process, to enable learning policies capable of controlling a universal set of continuously parameterized quantum gates. Finally, we provide benchmark comparisons to assess the shortcomings and strengths of current state-of-the-art RL algorithms.