{"ID":2832673,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.05946","arxiv_id":"2512.05946","title":"Variational Quantum Rainbow Deep Q-Network for Optimizing Resource Allocation Problem","abstract":"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/.","short_abstract":"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 Varia...","url_abs":"https://arxiv.org/abs/2512.05946","url_pdf":"https://arxiv.org/pdf/2512.05946v1","authors":"[\"Truong Thanh Hung Nguyen\",\"Truong Thinh Nguyen\",\"Hung Cao\"]","published":"2025-12-05T18:43:18Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.ET\",\"cs.SE\"]","methods":"[\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":606266,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2832673,"paper_url":"https://arxiv.org/abs/2512.05946","paper_title":"Variational Quantum Rainbow Deep Q-Network for Optimizing Resource Allocation Problem","repo_url":"https://github.com/Analytics-Everywhere-Lab/qtrl","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}