[Paper] Quantum Multiple Rotation Averaging

Source: arXiv - 2602.10115v1

Overview

The paper presents IQARS (Iterative Quantum Annealing for Rotation Synchronization), a novel way to solve the Multiple Rotation Averaging (MRA) problem that underpins many 3‑D vision and robotics pipelines. By recasting MRA as a series of binary quadratic sub‑problems that can run on quantum annealers, the authors demonstrate a measurable boost in accuracy over the strongest classical solvers today.

Key Contributions

• First quantum‑annealing formulation of MRA – translates the continuous rotation‑synchronization task into a sequence of binary quadratic problems suitable for D‑Wave‑style hardware.
• Iterative refinement pipeline (IQARS) – repeatedly solves locally quadratic, non‑convex sub‑problems, preserving the true SO(3) manifold geometry without relying on convex relaxations.
• Empirical evidence of superior accuracy – on both synthetic benchmarks and real‑world datasets, IQARS achieves roughly 12 % higher rotation‑estimation accuracy than Shonan, the best classical baseline evaluated.
• Open discussion of quantum‑hardware constraints – the work outlines practical limits (problem size, qubit connectivity, noise) and provides a roadmap for scaling as annealers mature.

Methodology

1. Problem Binarization – The continuous rotation variables are discretized into binary spin variables using a carefully designed encoding that respects the SO(3) structure.
2. Local Quadratic Sub‑Problems – Instead of tackling the full non‑convex MRA objective at once, IQARS breaks it into a series of local quadratic sub‑problems that each capture a small patch of the rotation graph (e.g., a few neighboring cameras).
3. Quantum Annealing Execution – Each sub‑problem is submitted to a D‑Wave quantum annealer, which searches for low‑energy spin configurations via quantum tunneling and massive parallelism.
4. Iterative Update & Re‑linearization – The binary solution is decoded back into a rotation estimate, the global objective is re‑linearized around this estimate, and the process repeats until convergence.
5. Hybrid Classical Post‑Processing – A lightweight classical refinement (e.g., a few IRLS steps) cleans up any residual errors, ensuring the final rotations lie exactly on the SO(3) manifold.

The pipeline is deliberately modular: developers can swap the quantum backend for a classical simulated annealer or a GPU‑accelerated QUBO solver to experiment with trade‑offs between speed and solution quality.

Results & Findings

• Accuracy gain: Across the board, IQARS reduces the average rotation error by ~12 % compared to Shonan.
• Robustness to noise: The advantage widens in high‑noise regimes where convex relaxations tend to break down.
• Runtime: For problem sizes fitting current hardware (≤ 150 rotations, ≤ 300 relative measurements), the quantum annealing step takes 0.5–2 seconds per iteration, comparable to a few IRLS iterations on a modern CPU.
• Scalability observations: When the problem exceeds the qubit count or connectivity limits, the authors resort to problem decomposition, which incurs modest overhead but still preserves the accuracy edge.

Practical Implications

• Improved 3‑D reconstruction pipelines – More accurate rotation estimates translate directly into tighter point‑cloud alignment, fewer outliers, and higher‑quality meshes for AR/VR, autonomous navigation, and cultural‑heritage digitization.
• Robotics & SLAM – Precise orientation synchronization can reduce drift in multi‑robot fleets or in long‑term SLAM sessions where loop closures are noisy.
• Hybrid quantum‑classical toolchains – IQARS showcases a concrete use‑case where a quantum annealer can be dropped into an existing C++/Python vision stack as a black‑box optimizer, offering a “quantum‑boost” without rewriting the whole pipeline.
• Future‑proofing for quantum hardware – As annealers scale to thousands of qubits and improve connectivity, the same IQARS framework can handle larger camera networks (e.g., city‑scale photogrammetry) with minimal algorithmic changes.

Developers interested in experimenting can start with the open‑source QUBO formulation provided by the authors, run it on D‑Wave’s Leap cloud service, and compare against a baseline IRLS implementation in their own codebase.

Limitations & Future Work

• Hardware constraints: Current D‑Wave devices support only a few hundred binary variables with limited connectivity, restricting the size of rotation graphs that can be solved in a single annealing call.
• Encoding overhead: The discretization step introduces approximation error; while the iterative scheme mitigates it, a tighter encoding could further improve results.
• Benchmark breadth: Experiments focus on synthetic and modest‑scale real datasets; large‑scale Structure‑from‑Motion (thousands of views) remains untested.
• Hybrid strategies: The authors suggest integrating quantum annealing with classical SDP relaxations or GPU‑accelerated QUBO solvers to bridge the gap until hardware matures.

Overall, the paper positions quantum annealing as a promising, albeit still early‑stage, accelerator for a core problem in computer vision and robotics, opening a concrete pathway for developers to experiment with quantum‑enhanced optimization today.

Authors

• Shuteng Wang
• Natacha Kuete Meli
• Michael Möller
• Vladislav Golyanik

Paper Information

• arXiv ID: 2602.10115v1
• Categories: cs.CV
• Published: February 10, 2026
• PDF: Download PDF