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Quantum Control Pulse Engineering Quantum Machine Learning

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

arXiv

Authors: Abhijit Sen, Sonali Panda, Mahima Arya, Subhajit Patra, Zizhan Zheng, Denys I. Bondar

Year

2026

Paper ID

3888

Status

Preprint

Abstract Read

~2 min

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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.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2026 reference point for readers tracking recent quantum research.
This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2601.08662 [2] arXiv https://arxiv.org/abs/2601.08662 [3] Publisher https://arxiv.org/abs/2601.08662

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External citation index: OpenAlex citation signal • updated 2026-11-04 00:26:28

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