Quantum Multiple Rotation Averaging

• URL: http://arxiv.org/abs/2602.10115v1
• Date: Tue, 10 Feb 2026 18:59:54 GMT
• Title: Quantum Multiple Rotation Averaging
• Abstract summary: Multiple quadratic averaging (MRA) is a fundamental optimization problem in 3D vision and robotics.<n>We introduce IQARS (Iterative Quantum Anneal Rotationing for Synchronization), the first algorithm that reformulates MRA as a sequence.<n>We evaluate IQARS's performance on synthetic and real-world datasets.
• Score: 28.27979152030028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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