Quantum Vision Clustering

TL;DR

This paper introduces a novel quantum computing-based clustering formulation tailored for adiabatic quantum computers, demonstrating its effectiveness and feasibility on current hardware for small-scale visual clustering tasks.

Contribution

It presents the first clustering formulation compatible with adiabatic quantum computing, including an Ising model representation and analysis of solution properties on real quantum hardware.

Findings

High competitiveness with state-of-the-art methods

Feasibility demonstrated on current quantum hardware

Effective for small-scale visual clustering

Abstract

Unsupervised visual clustering has garnered significant attention in recent times, aiming to characterize distributions of unlabeled visual images through clustering based on a parameterized appearance approach. Alternatively, clustering algorithms can be viewed as assignment problems, often characterized as NP-hard, yet precisely solvable for small instances on contemporary hardware. Adiabatic quantum computing (AQC) emerges as a promising solution, poised to deliver substantial speedups for a range of NP-hard optimization problems. However, existing clustering formulations face challenges in quantum computing adoption due to scalability issues. In this study, we present the first clustering formulation tailored for resolution using Adiabatic quantum computing. An Ising model is introduced to represent the quantum mechanical system implemented on AQC. The proposed approach demonstrates high competitiveness compared to state-of-the-art optimization-based methods, even when utilizing off-the-shelf integer programming solvers. Lastly, this work showcases the solvability of the proposed clustering problem on current-generation real quantum computers for small examples and analyzes the properties of the obtained solutions