{"ID":2876173,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00713","arxiv_id":"2509.00713","title":"It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensembles","abstract":"Quantum reinforcement learning (QRL) promises compact function approximators with access to vast Hilbert spaces, but its practical progress is slowed by NISQ-era constraints such as limited qubits and noise accumulation. We introduce a multi-chip ensemble framework using multiple small Quantum Convolutional Neural Networks (QCNNs) to overcome these constraints. Our approach partitions complex, high-dimensional observations from the Super Mario Bros environment across independent quantum circuits, then classically aggregates their outputs within a Double Deep Q-Network (DDQN) framework. This modular architecture enables QRL in complex environments previously inaccessible to quantum agents, achieving superior performance and learning stability compared to classical baselines and single-chip quantum models. The multi-chip ensemble demonstrates enhanced scalability by reducing information loss from dimensionality reduction while remaining implementable on near-term quantum hardware, providing a practical pathway for applying QRL to real-world problems.","short_abstract":"Quantum reinforcement learning (QRL) promises compact function approximators with access to vast Hilbert spaces, but its practical progress is slowed by NISQ-era constraints such as limited qubits and noise accumulation. We introduce a multi-chip ensemble framework using multiple small Quantum Convolutional Neural Netw...","url_abs":"https://arxiv.org/abs/2509.00713","url_pdf":"https://arxiv.org/pdf/2509.00713v1","authors":"[\"Junghoon Justin Park\",\"Huan-Hsin Tseng\",\"Shinjae Yoo\",\"Samuel Yen-Chi Chen\",\"Jiook Cha\"]","published":"2025-08-31T06:15:55Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"Convolutional Neural Network\"]","has_code":false}