From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial

Abhijit Sen, Sonali Panda, Mahima Arya, Subhajit Patra, Zizhan Zheng +1 more
1/13/2026
cs.AIquant-ph

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Abstract

This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations. It focuses on bridging the gap between RL theory and practical coding applications, addressing common challenges that students face when transitioning from conceptual understanding to implementation. Through hands-on examples and approachable explanations, the tutorial aims to equip students with the foundational skills needed to confidently apply RL techniques in real-world scenarios.

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Cite this paper

@article{sen2026from,
  title  = {From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial},
  author = {Abhijit Sen and Sonali Panda and Mahima Arya and Subhajit Patra and Zizhan Zheng and Denys I. Bondar},
  year   = {2026},
  eprint = {2601.08662},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.08662v2}
}

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