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

arXiv: https://arxiv.org/abs/2601.08662 · HTML: https://mlsanity.com/paper/2601.08662/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 90 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [Quantum reinforcement learning](https://mlsanity.com/deeprl/quantum-reinforcement-learning/)

## Builds on (most cited first, 15 of 15)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Sample Efficient Reinforcement Learning with REINFORCE](https://mlsanity.com/paper/2010.11364/) (2020)
- [Universal Quantum Control through Deep Reinforcement Learning](https://mlsanity.com/paper/1803.01857/) (2018)
- [Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?](https://mlsanity.com/paper/2005.00527/) (2020)
- [An Invitation to Deep Reinforcement Learning](https://mlsanity.com/paper/2312.08365/) (2023)
- [Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search](https://mlsanity.com/paper/2412.18208/) (2024)
- [Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning](https://mlsanity.com/paper/2501.04879/) (2025)
- [Globally optimal control of quantum dynamics](https://mlsanity.com/paper/2209.05790/) (2022)

## Abstract (arXiv)

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.