[論文レビュー] From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial

Abhijit Sen, Sonali Panda+4 more|arXiv (Cornell University)|Jan 13, 2026

Quantum Computing Algorithms and Architecture被引用数 0

ひとこと要約

初心者向けのRLチュートリアルで、理論とコードを結びつけ、シンプルな例を通じてRL手法が量子制御にどう適用されるかを示し、すぐに使えるコードを提供する。

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.

研究の動機と目的

• Provide an accessible, example-driven introduction to reinforcement learning (RL) and its essential concepts.
• Bridge theory and implementation by including clear mathematical explanations and ready-to-use code.
• Demonstrate how RL techniques can be applied to high-fidelity quantum state manipulation and quantum control.

提案手法

• Present fundamental RL concepts (policies, transition probabilities, value and action-value functions, episodes, trajectories, discounting).
• Explain essential probabilistic prerequisites (probability, conditional probability, random variables, expectations).
• Introduce policy evaluation and policy improvement, including Monte Carlo and temporal-difference methods.
• Cover direct policy optimization techniques (policy gradient and actor-critic) for continuous action spaces.
• Connect RL methods to quantum control by outlining how they enable efficient, high-fidelity manipulation of quantum states.
• Provide ready-to-use Python code and step-by-step examples to reinforce learning.

実験結果

リサーチクエスチョン

• RQ1 How can classical RL concepts (policies, value functions, and policy improvement) be taught in a beginner-friendly, example-driven way?
• RQ2 What are the essential probabilistic foundations needed to understand RL, and how are they applied in RL algorithms?
• RQ3 How do Monte Carlo and temporal-difference methods compare for policy evaluation in simple environments?
• RQ4 How can RL techniques be adapted to perform quantum state control and manipulation with high fidelity?

主な発見

• The tutorial emphasizes a single simple example to teach all main RL concepts in a connected, easy-to-follow way.
• It provides clear mathematical explanations alongside ready-to-use code to bridge theory and implementation.
• It covers foundational RL topics (MDP, dynamic programming, Monte Carlo, TD, policy gradient, actor-critic).
• It discusses deterministic and non-deterministic transitions and introduces state-reward transitions for richer modeling.
• It explains how RL methods can enable efficient, high-fidelity manipulation of quantum states for quantum control.