mlsanityData to 14 Oct 2026

Variational Quantum Rainbow Deep Q-Network for Optimizing Resource Allocation Problem

arXiv 2512.05946 ↗First posted 5 Dec 2025cs.AI cs.ET cs.SEcited by 0

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Abstract (arXiv)

Resource allocation remains NP-hard due to combinatorial complexity. While deep reinforcement learning (DRL) methods, such as the Rainbow Deep Q-Network (DQN), improve scalability through prioritized replay and distributional heads, classical function approximators limit their representational power. We introduce Variational Quantum Rainbow DQN (VQR-DQN), which integrates ring-topology variational quantum circuits with Rainbow DQN to leverage quantum superposition and entanglement. We frame the human resource allocation problem (HRAP) as a Markov decision process (MDP) with combinatorial action spaces based on officer capabilities, event schedules, and transition times. On four HRAP benchmarks, VQR-DQN achieves 26.8% normalized makespan reduction versus random baselines and outperforms Double DQN and classical Rainbow DQN by 4.9-13.4%. These gains align with theoretical connections between circuit expressibility, entanglement, and policy quality, demonstrating the potential of quantum-enhanced DRL for large-scale resource allocation. Our implementation is available at: https://github.com/Analytics-Everywhere-Lab/qtrl/.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 41 references that are in the map. First posted on arXiv 5 Dec 2025.

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References · 9

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  4. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  5. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  6. 2021Parametrized quantum policies for reinforcement learningcited by 90
  7. 2019Exploratory Combinatorial Optimization with Reinforcement Learningcited by 82
  8. 2022A Survey on Quantum Reinforcement Learningcited by 53
  9. 2019Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environmentscited by 18

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