Outlier-Robust Multi-Model Fitting on Quantum Annealers

2025-04-18

AI Summary

Multi-model fitting (MMF) in the presence of a high proportion of outliers remains challenging for existing quantum approaches, which either assume outlier-free data or support only single-model fitting. Method: This paper reformulates MMF as a maximum set cover problem and proposes R-QuMF—the first quantum multi-model fitting algorithm robust to outliers—built upon the adiabatic quantum computation (AQC) framework. R-QuMF jointly estimates the optimal number of models and fits them without requiring prior knowledge of model count, leveraging native quantum annealing hardware to solve the combinatorial optimization formulation. Contribution/Results: Unlike prior quantum MMF methods, R-QuMF achieves adaptive model selection and strong robustness under severe outlier contamination. Extensive experiments on diverse synthetic and real-world 3D datasets demonstrate that R-QuMF significantly outperforms state-of-the-art quantum MMF algorithms, validating the efficacy and promise of quantum computing for noisy, complex vision-based fitting tasks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Multi-modal Vision
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Multi-model fitting (MMF) presents a significant challenge in Computer Vision, particularly due to its combinatorial nature. While recent advancements in quantum computing offer promise for addressing NP-hard problems, existing quantum-based approaches for model fitting are either limited to a single model or consider multi-model scenarios within outlier-free datasets. This paper introduces a novel approach, the robust quantum multi-model fitting (R-QuMF) algorithm, designed to handle outliers effectively. Our method leverages the intrinsic capabilities of quantum hardware to tackle combinatorial challenges inherent in MMF tasks, and it does not require prior knowledge of the exact number of models, thereby enhancing its practical applicability. By formulating the problem as a maximum set coverage task for adiabatic quantum computers (AQC), R-QuMF outperforms existing quantum techniques, demonstrating superior performance across various synthetic and real-world 3D datasets. Our findings underscore the potential of quantum computing in addressing the complexities of MMF, especially in real-world scenarios with noisy and outlier-prone data.

Problem

• Addresses outlier-robust multi-model fitting in computer vision
• Leverages quantum computing for NP-hard combinatorial challenges
• Eliminates need for prior knowledge of model count

Innovation

• Quantum annealing for multi-model fitting
• Robust outlier handling without prior knowledge
• Maximum set coverage formulation for AQC