🗃 This repository contains Python functions and processing pipelines documented in Jupyter notebook for pixel-wise binary classification of remote sensing multispectral images with the D-Wave Advantage quantum annealer.
### Current publication
More information can be found in the conference paper connected to this repository
📜 Amer Delilbasic, Gabriele Cavallaro, Madita Willsch, Farid Melgani, Morris Riedel and Kristel Michielsen, “Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2020 (accepted).
Recent developments in Quantum Computing (QC) have paved the way for an enhancement of computing capabilities. Quantum Machine Learning (QML) aims at developing Machine Learning (ML) models specifically designed for quantum computers. The availability of the first quantum processors enabled further research, in particular the exploration of possible practical applications of QML algorithms. In this work, quantum formulations of the Support Vector Machine (SVM) are presented. Then, their implementation using existing quantum technologies is discussed and Remote Sensing (RS) image classification is considered for evaluation.
## Previous publications
### Previous publications
📃 D. Willsch, M. Willsch, H. De Raedt, and K. Michielsen, “Support Vector Machines on the D-Wave Quantum Annealer” in Computer Physics Communications, vol. 248, 2020, https://doi.org/10.1016/j.cpc.2019.107006
📃 G. Cavallaro, D. Willsch, M. Willsch, K. Michielsen, and M. Riedel, “Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1973-1976, 2020, https://doi.org/10.1109/IGARSS39084.2020.9323544
## D-Wave Leap
### D-Wave Leap
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