自适应旋转用于iSOMA：变分量子目标中的几何、基准测试与噪声鲁棒性
Adaptive Rotation for iSOMA: Geometry, Benchmarking, and Noise Robustness in Variational Quantum Objectives
- VSB-Technical University of Ostrava（俄斯特拉发科技大学）
- IT4Innovations National Supercomputing Center, VSB-Technical University of Ostrava（俄斯特拉发科技大学 IT4创新国家超级计算中心）
- Marine Research Institute, Klaipeda University（克莱佩达大学海洋研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出iSOMA-AR，通过从成功迁移位移中学习基来降低iSOMA的坐标依赖性，在BBOB和CEC 2011基准上表现优异，但在噪声环境下优势不显著，表明自适应旋转主要利于坐标敏感的确定性优化。
AI中文摘要：
我们研究了改进型自组织迁移算法（iSOMA）的坐标依赖性是否能在保留其廉价的领导者引导迁移机制的同时得到降低。我们引入了iSOMA-AR，它从成功的迁移位移中学习一个基，并选择性地在该基上应用标准扰动掩码。在完整的无噪声BBOB测试套件上，iSOMA-AR在匹配条件下显著优于基线iSOMA，在几何困难的景观上提升最大。一项针对性的消融研究表明，学习到的方向在旋转的病态景观上是有益的，并且门限阈值和旋转上限的适度变化保持了定性结果。在CEC 2011真实世界优化问题上，iSOMA-AR在大多数问题上优于iL-SHADE，尽管其相对于基线iSOMA的优势在统计上不显著。作为事后敏感性检查，报告了规范jSO的重新运行，以及原始jSO衍生的比较器。在受挫自旋变分量子目标上，自适应旋转改善了大多数横场条件，而在对角和各向异性模型上的增益则缺失或具有选择性。在强有效采样噪声下，SOMA变体是比较中最稳健的基于群体的方法，但iSOMA-AR并未显著优于基线iSOMA。修复全零PRT掩码大大减少了重复点评估，而不显著改变端点质量，使得这一实现细节不太可能解释噪声结果。总体而言，自适应旋转在坐标敏感的确定性问题上最有用，而观察到的噪声鲁棒性似乎主要源于底层的SOMA迁移机制。
英文摘要：
We study whether the coordinate dependence of the improved Self-Organizing Migrating Algorithm (iSOMA) can be reduced while retaining its inexpensive leader-directed migration mechanism. We introduce iSOMA-AR, which learns a basis from successful migration displacements and selectively applies the standard perturbation mask in that basis. On the complete noiseless BBOB suite, iSOMA- AR significantly outperformed baseline iSOMA across matched conditions, with the largest gains on geometrically difficult landscapes. A targeted ablation shows that the learned orientation is beneficial on a rotated ill-conditioned landscape and that moderate changes of the gate threshold and rotation cap preserve the qualitative result. On CEC 2011 Real World Optimization Problems, iSOMA-AR outperformed iL-SHADE on most problems, although its advantage over baseline iSOMA was not statistically significant. A canonical-jSO rerun is reported as a post-hoc sensitivity check alongside the original jSO-derived comparator. On frustrated-spin variational quantum objectives, adaptive rotation improved most transverse-field conditions, while gains on the diagonal and anisotropic models were absent or selective. Under strong effective sampling noise, the SOMA variants were the most robust population-based methods in the comparison, but iSOMA-AR was not significantly better than baseline iSOMA. Repairing all-zero PRT masks greatly reduced repeated-point evaluations without changing endpoint quality significantly, making this implementation detail unlikely to explain the noise result. Overall, adaptive rotation is most useful on coordinate-sensitive deterministic problems, while the observed noise robustness appears to arise mainly from the underlying SOMA migration mechanism.
