Jan-Nico Zaech, Martin Danelljan, Tolga Birdal, Luc Van Gool

IEEE Conference on Computer Vision and Pattern Recognition 2024 (CVPR)

Abstract

Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems. Current AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for many computer vision tasks. Despite requiring multiple measurements from the noisy AQC, current approaches only utilize the best measurement, discarding information contained in the remaining ones. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little additional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.

I am an organizer of the workshop on Quantum Computer Vision and Machine Learning at CVPR 2023 in Vancouver, Canada.

Our goal is to introduce and promote the exciting field of quantum computing to computer vision, as well as provide a platform for researchers interested in this area to connect and exchange ideas. Get ready for half a day of tutorials, invited talks, and a poster session that highlights early work in the field.

Jan-Nico Zaech, Alexander Liniger, Martin Danelljan, Dengxin Dai, Luc Van Gool

Conference on Computer Vision and Pattern Recognition, CVPR 2022

Abstract

Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. The association step naturally leads to discrete optimization problems. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. Adiabatic quantum computing (AQC) offers a solution for this, as it has the potential to provide a considerable speedup on a range of NP-hard optimization problems in the near future. However, current MOT formulations are unsuitable for quantum computing due to their scaling properties. In this work, we therefore propose the first MOT formulation designed to be solved with AQC. We employ an Ising model that represents the quantum mechanical system implemented on the AQC. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers. Finally, we demonstrate that our MOT problem is already solvable on the current generation of real quantum computers for small examples, and analyze the properties of the measured solutions.