Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits

arXiv:2605.03434 · cs.LG, quant-ph · Submitted 2026-05-05 · Read on arXiv

cs.LG, quant-ph

Submitted: 2026-05-05

Updated: 2026-09-19

Code: https://github.com/Yu-TingLee/quantum-option-critic

License: http://creativecommons.org/licenses/by/4.0/

The gist: While parameterized quantum computations have shown success in standard reinforcement learning (RL), whether these advantages adapt to hierarchical RL (HRL) remains a critical open question.

Terminology

• Reinforcement Learning — 39× in this paper · explained in 4 episodes
• Quantum Circuits — 18× in this paper · explained in 2 episodes
• Actor — 15× in this paper · explained in 3 episodes
• Abstraction — 9× in this paper · explained in 1 episode
• Entanglement — 8× in this paper · explained in 3 episodes
• Temporal Abstraction — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 6× in this paper · explained in 2 episodes
• Hybrid Models — 6× in this paper · explained in 2 episodes
• Q-learning — 5× in this paper · explained in 3 episodes
• Action Space — 4× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Active Learning — 2× in this paper · explained in 6 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Actor-Critic Architecture — 1× in this paper · explained in 1 episode
• Deep Q-Network — 1× in this paper
• Degeneracy — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hybrid System — 1× in this paper · explained in 2 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes

Abstract

Sources

• A Survey on Quantum Reinforcement Learning
• RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner
• Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• Adam: A Method for Stochastic Optimization

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