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 challenges. Furthermore, we propose 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.
@inproceedings{ altmannQCE24,
author = "Philipp Altmann and Jonas Stein and Michael Kölle and Adelina Bärligea and Maximilian Zorn and Thomas Gabor and Thomy Phan and Sebastian Feld and Claudia Linnhof-Popien",
title = "Challenges for Reinforcement Learning in Quantum Circuit Design",
year = "2024",
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 challenges. Furthermore, we propose 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.",
url = "https://arxiv.org/pdf/2312.11337.pdf",
eprint = "https://arxiv.org/pdf/2312.11337.pdf",
booktitle = "Proceedings of the IEEE International Conference on Quantum Computing and Engineering",
}
Related Articles
- P. Altmann et al., “Quantum Circuit Design: A Reinforcement Learning Challenge”, AAMAS 2024 (conference version)
- T. Phan et al., “Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability”, ICML 2023
- P. Altmann et al., “CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing”, IJCAI 2023
- P. Altmann et al., “DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training”, ALA 2023
- T. Gabor et al., “Scenario Co-Evolution for Reinforcement Learning on a Grid World Smart Factory Domain”, GECCO 2019
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