It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensembles
Year: 2025
Type: conference-paper
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 multichip 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Korea Brain Research Institute, Seoul National University, Ministry of Education, Korea Health Industry Development Institute, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Samsung, Institute for Information and Communications Technology Promotion, National Supercomputing Center, Korea Institute of Science and Technology Information
Awards: G1029905031, G1113160982, G1152552616, G120387841, G1610201404, G2198004682, G2511155774, G2586176782, G2806721493, G2947324332, G3031715232, G347960030, G3812690899, G393097410, G4475359334, G5065061179, G5287361696, G5635159257, G6052186190, G6120464043, G6246450888, G6521983585, G658211837, G67931008, G7050094077, G7358857777, G786726864, G7978168759, G8029776622, G8978267545
OpenAlex ID: W7125588481