Challenges for Reinforcement Learning in Quantum Circuit Design

• URL: http://arxiv.org/abs/2312.11337v3
• Date: Thu, 8 Aug 2024 08:44:52 GMT
• Title: Challenges for Reinforcement Learning in Quantum Circuit Design
• Authors: Philipp Altmann, Jonas Stein, Michael Kölle, Adelina Bärligea, Thomas Gabor, Thomy Phan, Sebastian Feld, Claudia Linnhoff-Popien,
• Abstract summary: Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. 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.
• Score: 8.894627352356302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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