Challenges for Reinforcement Learning in Quantum Circuit Design
Philipp Altmann (Ludwig Maximilians University)
Jonas Stein (Ludwig Maximilians University)
Michael Kolle (Ludwig Maximilians University)
Adelina Barligea (Technische Universität München)
Maximilian Zorn (Ludwig Maximilians University)
Thomas Gabor (Ludwig Maximilians University)
Thomy Phan (University of Southern California)
S. Feld (TU Delft - Quantum Circuit Architectures and Technology, TU Delft - QCD/Feld Group)
Claudia Linnhoff-Popien (Ludwig Maximilians University)
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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 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.
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