It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensembles
This work addresses scalability and noise issues in quantum reinforcement learning for complex tasks like video games, offering a practical pathway for near-term quantum hardware, though it is incremental in its modular approach.
The paper tackled the challenge of applying quantum reinforcement learning to complex environments by introducing a multi-chip ensemble framework that partitions high-dimensional observations across independent quantum circuits and aggregates them classically, achieving superior performance and learning stability compared to classical baselines and single-chip quantum models.
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.