Adaptive hybrid quantum-classical computing framework for deep space exploration mission applications

Quantum computing presents transformative potential for solving complex problems in industrial systems, particularly through its application in space mission operations. However, the practical deployment of fully quantum systems faces substantial challenges due to hardware noise, decoherence, and limited qubit coherence times. To address this challenge, this study proposes a framework for hybrid quantum-classical computing tailored to space systems' unique demands. The framework integrates quantum sensors, processors, and communication components with conventional spacecraft computing systems to overcome quantum hardware constraints. Through quantum-classical computing integration, the framework enhances operational efficiency and information integration essential for complex space mission operations. We discuss the critical components and integration interfaces of the hybrid framework and demonstrate its application through a case study on satellite imaging task scheduling. We implement the Quantum Approximate Optimization Algorithm (QAOA) and IBM's Qiskit quantum simulator to solve the scheduling task scheduling problem. Results obtained from the simulation demonstrate enhanced optimization capabilities compared to a greedy algorithm. The results highlight the advantages of information integration between quantum and classical systems for solving complex satellite scheduling tasks.