用最大割问题的参数，加速独立集问题求解。
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach
- 用机器学习筛选适合转移的MaxCut参数
- 转移后可减少约70%优化迭代次数
- 适合量子近似优化算法初学者和资源受限场景
量子近似优化算法（QAOA）是实现组合优化量子优势的有力候选。由于存在贫瘠平原等问题，寻找良好的变分参数集极具挑战。因此，参数迁移成为研究热点：将一个实例优化好的参数集迁移到更复杂的问题中，以加速求解或作为优化起点。本文探讨能否将已知的MaxCut问题预训练参数直接用于最大独立集（MIS）问题。通过设计机器学习模型，筛选在MaxCut上优化良好的参数集，并将其应用于MIS电路。实验表明，该方法显著减少了优化迭代次数，同时保持了相近的近似比，验证了跨问题参数迁移的有效性。
原文摘要 · Abstract (English)
Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising candidates to achieve the quantum advantage in solving combinatorial optimization problems. The process of finding a good set of variational parameters in the QAOA circuit has proven to be challenging due to multiple factors, such as barren plateaus. As a result, there is growing interest in exploiting parameter transferability, where parameter sets optimized for one problem instance are transferred to another that could be more complex either to estimate the solution or to serve as a warm start for further optimization. But can we transfer parameters from one class of problems to another? Leveraging parameter sets learned from a well-studied class of problems could help navigate the less studied one, reducing optimization overhead and mitigating performance pitfalls. In this paper, we study whether pretrained QAOA parameters of MaxCut can be used as is or to warm start the Maximum Independent Set (MIS) circuits. Specifically, we design machine learning models to find good donor candidates optimized on MaxCut and apply their parameters to MIS acceptors. Our experimental results show that such parameter transfer can significantly reduce the number of optimization iterations required while achieving comparable approximation ratios.
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