From d1f27cda9bfff83bf74e0bf09356d13b37b676fd Mon Sep 17 00:00:00 2001 From: Gabriele Cavallaro <g.cavallaro@fz-juelich.de> Date: Sat, 22 May 2021 06:54:18 +0000 Subject: [PATCH] Update README.md --- README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 002fe67..cad6792 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,9 @@ ## General information -🗃 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. +🗃 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 @@ -10,13 +12,13 @@ More information can be found in the conference paper connected to this reposito 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 👌Everyone can make a free account to run on the D-Wave Advantage quantum annealer: -- GitLab