Challenges for Reinforcement Learning in Quantum Circuit Design

arXiv:2312.11337v2 Announce Type: replace-cross
Abstract: 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 the search for viable quantum architectures, which we formalize by a set of generic …

abstract advantages application applications arxiv challenges computing cs.lg current design hybrid insight machine machine learning nisq precision qml quant-ph quantum quantum computing reinforcement reinforcement learning type