From Classical to Quantum Reinforcement Learning: A Friendly Beginner's Guide

Kari Jaaskelainen

14 Jan 2026 — 1 min read

Curious how the algorithms that learn to play games can also steer quantum devices? This beginner-friendly tutorial walks you from classical reinforcement learning (RL) basics to quantum RL, showing how these ideas power practical tasks in quantum control.

What makes it different:

Plain-English explanations that connect math to intuition.
Step-by-step, code-oriented examples that turn concepts into working implementations.
Guidance on common roadblocks students face when moving from theory to practice.
A bridge to quantum settings, so you can see how RL strategies adapt to controlling quantum systems.

Perfect for undergrads and curious builders: you’ll gain the confidence to prototype, experiment, and understand why RL decisions work—not just how to run them.

Open tutorial: https://arxiv.org/abs/2601.08662v1 — by Abhijit Sen, Sonali Panda, Mahima Arya, Subhajit Patra, Zizhan Zheng, and Denys I. Bondar.

Paper: https://arxiv.org/abs/2601.08662v1

Register: https://www.AiFeta.com

ReinforcementLearning QuantumComputing AI Education Tutorial QuantumControl MachineLearning Undergraduate Coding

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