Related papers: Quantum Robust Fitting

Quantum Robust Fitting

URL: http://arxiv.org/abs/2006.06986v3
Date: Fri, 9 Oct 2020 11:02:05 GMT
Title: Quantum Robust Fitting
Authors: Tat-Jun Chin, David Suter, Shin-Fang Chng, James Quach
Abstract summary: Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. In this paper, we explore the usage of quantum computers for robust fitting.
Score: 43.28199403216451
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by Fourier analysis of Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.

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