Satellite Constellation Scheduling with Quantum Reinforcement Learning

초록

Reinforcement learning (RL) utilizing classical neural networks (NNs) has achieved substantial progress across numerous application domains. Nevertheless, classical RL faces significant challenges in training when applied to systems with high-dimensional action spaces, such as coordinated satellite networks. In these scenarios, the exponential increase in model parameters imposes heavy computational requirements, thereby constraining scalability and slowing convergence. Quantum reinforcement learning (QRL), which incorporates quantum neural networks (QNNs), provides a promising alternative by exploiting quantum mechanical properties, including superposition and entanglement. Through QNNs, multiple states can be compactly represented using a limited number of quantum bits (qubits), effectively reducing computational overhead. Due to these characteristics—namely, rapid convergence and enhanced scalability—QRL constitutes a suitable substitute for classical RL in coordinated satellite applications. Moreover, the proposed QRL framework mitigates the curse of dimensionality by efficiently leveraging qubit-based representations. In experimental environments characterized by high-dimensional action spaces, the proposed algorithm demonstrates superior training performance compared to conventional RL approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

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