From classical to quantum reinforcement learning and its applications in quantum control: A beginner’s tutorial
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
Fields of Study & Subjects
Quantum Computing Algorithms and Architecture
Reinforcement Learning in Robotics
Quantum Information and Cryptography
Publication details
- Journal / source
- Low Temperature Physics
- Volume
- 52
- Issue
- 9
- Pages
- 999–1022
- Publication date
- 1 September 2026
- Language
- EN