用量子退火解决旋转平均问题，提升高噪声下的精度。
Quantum Multiple Rotation Averaging
- 将旋转平均转为可上量子退火器的二次非凸子问题
- 在真实数据上比最优经典方法高12%准确率
- 适合对高噪声下旋转估计精度有要求的研究者
多旋转平均（MRA）是3D视觉与机器人中的基础优化问题，旨在从噪声相对测量中恢复全局一致的绝对旋转。传统经典方法如L1-IRLS和Shonan存在易陷入局部极小、依赖凸松弛导致流形几何失真等问题，尤其在高噪声下性能下降。本文提出IQARS（迭代量子退火旋转同步），首次将MRA重构成一系列可在量子退火器上求解的局部二次非凸子问题，通过二值化实现。该方法摆脱了凸松弛依赖，更精确保留非欧旋转流形结构，并利用量子隧穿与并行性高效探索解空间。我们在合成与真实数据集上评估了IQARS性能。尽管当前退火器尚处早期阶段，仅支持有限规模问题且性能受限，但在D-Wave退火器上已实现约12%的精度提升，优于实测中表现最佳的经典方法Shonan。
原文摘要 · Abstract (English)
Multiple rotation averaging (MRA) is a fundamental optimization problem in 3D vision and robotics that aims to recover globally consistent absolute rotations from noisy relative measurements. Established classical methods, such as L1-IRLS and Shonan, face limitations including local minima susceptibility and reliance on convex relaxations that fail to preserve the exact manifold geometry, leading to reduced accuracy in high-noise scenarios. We introduce IQARS (Iterative Quantum Annealing for Rotation Synchronization), the first algorithm that reformulates MRA as a sequence of local quadratic non-convex sub-problems executable on quantum annealers after binarization, to leverage inherent hardware advantages. IQARS removes convex relaxation dependence and better preserves non-Euclidean rotation manifold geometry while leveraging quantum tunneling and parallelism for efficient solution space exploration. We evaluate IQARS's performance on synthetic and real-world datasets. While current annealers remain in their nascent phase and only support solving problems of limited scale with constrained performance, we observed that IQARS on D-Wave annealers can already achieve ca. 12% higher accuracy than Shonan, i.e., the best-performing classical method evaluated empirically.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。