Quantum Vision Clustering
This work addresses scalability issues in quantum computing for clustering, offering a novel formulation that could enable speedups for NP-hard optimization problems in unsupervised visual tasks, though it is incremental as it builds on existing quantum computing paradigms.
The authors tackled the challenge of adapting clustering algorithms for quantum computing by introducing the first clustering formulation specifically designed for Adiabatic Quantum Computing (AQC), demonstrating competitive performance against state-of-the-art optimization methods and proving solvability on current quantum hardware for small instances.
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