arXiv Machine Learning September 10, 2026 By Yu-Ting Lee, Samuel Yen-Chi Chen, Fu-Chieh Chang

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits

Read the original on arXiv Machine Learning →

The paper investigates the use of variational quantum circuits (VQCs) in hierarchical reinforcement learning (HRL). It shows that a hybrid HRL agent incorporating a quantum feature extractor can outperform classical baselines with fewer parameters, but using VQCs for option-value estimation hampers learning. The study also explores how different quantum circuit designs influence performance and proposes design principles for efficient hybrid HRL agents.

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