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From classical to quantum reinforcement learning and its applications in quantum control: A beginner’s tutorial

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

Year

2026

Paper ID

77851

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

81

Citations

0

Abstract

This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students and researchers by offering clear, example-driven explanations. It focuses on bridging the gap between RL theory and practical coding, addressing common challenges that one faces when transitioning from conceptual understanding to implementation. Through hands-on examples and approachable explanations, the tutorial aims to equip the reader with the foundational skills needed to confidently apply RL techniques in real-world scenarios. Code Availability: https://github.com/asen009/Reinforcement_Tutorial_Undergraduates

Why This Paper Matters

  • This paper contributes to the Quantum Foundations 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 and researchers by offering clear, example-driven explanations.

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Current Paper #77851 #78817 Modelling Josephson Junctions w... #78814 Construction of isoscattering n... #78811 Autonomous Code Migration for Q... #78808 Experimental generation of asym...

External citation index: OpenAlex citation signal • updated 2026-10-19 19:19:33

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