结合量子计算与序列凸规划的低推力轨道优化
Low-Thrust Trajectory Optimization with Quantum Computing and Sequential Convex Programming
- Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于量子计算的序列凸规划框架quSCP，在地球-火星低推力转移任务中验证其性能，显示量子与混合量子-经典优化可为轨道设计提供有竞争力的方案。
AI中文摘要：
低推力轨道优化是行星际任务设计中的核心任务，但其非线性动力学与运行约束常构成极具挑战性的非凸最优控制问题。序列凸规划已成为解决此类问题的有效方法，而量子退火则为求解无约束二次二元优化问题提供了互补范式。本文提出了quSCP，一种基于量子计算的序列凸规划框架，该框架将每个凸子问题重新表述为适用于量子及量子-经典混合求解器的无约束二次二元优化问题。等式约束、不等式约束及信赖域约束通过二次惩罚项嵌入，同时采用迭代优化策略以降低二元离散化带来的精度损失。该方法在燃料最优的地球-火星低推力转移任务中进行评估，与标准序列凸规划、连续无约束二次公式、直接量子处理单元采样及D-Wave混合求解器对比。结果显示，quSCP生成的轨道符合物理一致性，推进剂消耗与非线性约束违反情况接近经典基准。直接量子退火因嵌入开销与硬件连通性限制仅适用于小规模实例，而混合求解器可扩展至更大的离散化规模。尽管当前硬件未展现计算量子优势，但结果表明量子与量子-经典混合优化已能为高要求的轨道设计问题提供具有竞争力的解决方案。
英文摘要：
Low-thrust trajectory optimization is a central task in interplanetary mission design, but its nonlinear dynamics and operational constraints often lead to challenging non-convex optimal-control problems. Sequential convex programming has emerged as an effective approach to address these problems, while quantum annealing offers a complementary paradigm for solving quadratic unconstrained binary optimization problems. This paper introduces quSCP, a quantum-based sequential convex programming framework that reformulates each convex subproblem as a quadratic unconstrained binary optimization problem suitable for quantum and hybrid quantum--classical solvers. Equality, inequality, and trust-region constraints are embedded through quadratic penalty terms, while an iterative refinement strategy is used to reduce the accuracy loss introduced by binary discretization. The method is assessed on a fuel-optimal Earth--Mars low-thrust transfer by comparing standard sequential convex programming, a continuous quadratic unconstrained formulation, direct quantum processing unit sampling, and D-Wave hybrid solvers. Results show that quSCP produces physically consistent trajectories with propellant consumption and nonlinear constraint violations close to classical benchmarks. Direct quantum annealing is feasible only for small instances because of embedding overhead and hardware connectivity limits, whereas hybrid solvers scale to larger discretizations. Although no computational quantum advantage is demonstrated with current hardware, the results show that quantum and hybrid quantum--classical optimization can already provide competitive solutions for demanding trajectory design problems.
