Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification
General information
Current publication
More information can be found in the conference paper connected to this repository
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
D-Wave Leap
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Make a free account to run on the D-Wave through
(https://www.dwavesys.com/take-leap -
Install Ocean Software with 'pip install dwave-ocean-sdk'
https://docs.ocean.dwavesys.com/en/latest/overview/install.html -
Configuring the D-Wave System as a Solver with 'dwave config create'
https://docs.ocean.dwavesys.com/en/stable/overview/sapi.html
Experiments
Praparation of the binary classification problem
The binary classification problem is constructed from the SemCity Toulouse multispectral benchmark data set, that is publicly available
More information about the dataset can be found in the publication below
R. Roscher, M. Volpi, C. Mallet, L. Drees, and J. D. Wegner, “Semcity toulouse: a benchmark for building instance segmentation in satellite images,” Isprs annals of photogrammetry, remote sensing and spatial information sciences, vol. V-5-2020, p. 109–116, 2020.
(1) Follow the instructions of the Jupyter Notebook