It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensembles

• URL: http://arxiv.org/abs/2509.00713v1
• Date: Sun, 31 Aug 2025 06:15:55 GMT
• Title: It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensembles
• Authors: Junghoon Justin Park, Huan-Hsin Tseng, Shinjae Yoo, Samuel Yen-Chi Chen, Jiook Cha,
• Abstract summary: Quantum reinforcement learning (QRL) promises compact function approximators with access to vast Hilbert spaces.<n>We introduce a multi-chip ensemble framework using multiple small Quantum Convolutional Neural Networks (QCNNs) to overcome constraints.<n>Our approach partitions complex, high-dimensional observations from the Super Mario Bros environment across independent quantum circuits.
• Score: 29.944281778572876
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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