mlsanityData to 7 Oct 2026

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits

arXiv 2605.03434 ↗First posted 5 May 2026cs.LG quant-phcited by 2

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Abstract (arXiv)

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. This work demonstrates that variational quantum circuits (VQCs) can effectively enhance HRL agents based on the option-critic architecture. Evaluated in standard environments, a hybrid HRL agent with a quantum feature extractor outperforms classical baselines while using fewer parameters. We also identify an architectural bottleneck: using VQCs for option-value estimation severely degrades learning. Further ablations reveal how quantum circuit design affects performance. Our work establishes design principles for parameter-efficient hybrid HRL agents.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 51 references that are in the map. First posted on arXiv 5 May 2026.

Builds on

31 of 51 references.

References · 31

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19 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Multivariate Time Series Forecasting with Adaptive Non-Local Observablescited by 1
  2. 2026Learning to Program Adaptive Non-Local Observables for Machine Learningcited by 0