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

2026-01-13

AI Summary

This work addresses the common challenge undergraduate students face in reinforcement learning—namely, the disconnect between theory and practice—by proposing a beginner-friendly instructional framework that systematically bridges classical and quantum reinforcement learning through example-driven pedagogy, with a focus on quantum control applications. The framework integrates classical algorithms, quantum reinforcement learning methodologies, Python programming, and quantum simulation techniques to provide an accessible, hands-on learning pathway. By doing so, it significantly lowers the entry barrier for novices, effectively narrows the gap between theoretical understanding and practical implementation, and offers preliminary validation of the feasibility of applying reinforcement learning to quantum control tasks.

Technology Category

• Machine Learning: Reinforcement Learning
• Reasoning under Uncertainty: Sequential Decision Making
• Intelligent Robots: Behavior Learning & Control

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• reinforcement learning
• undergraduate education
• implementation gap
• tutorial
• hands-on learning

Innovation

• reinforcement learning
• tutorial
• hands-on examples
• educational approach
• implementation