Quantum Dimension Reduction for Pattern Recognition in High-Resolution Spatio-Spectral Data

The promises of advanced quantum computing technology have driven research in the simulation of quantum computers on classical hardware, where the feasibility of quantum algorithms for real-world problems can be investigated. In domains such as High Energy Physics (HEP) and Remote Sensing Hyperspect...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on computers Jg. 71; H. 1; S. 1 - 12
Hauptverfasser:	Mahmud, Naveed, Haase-Divine, Bennett, MacGillivray, Andrew, El-Araby, Esam
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Circuit design Computers Datasets Dimensionality reduction Feasibility Field programmable gate arrays field-programmable gate arrays (FPGAs) Haar transformations Hardware High resolution Hyperspectral imaging Image resolution Logic gates Methodology Pattern recognition Quantum computers Quantum computing Qubit Reduction Remote sensing Search algorithms Search methods Spatial resolution Transforms
ISSN:	0018-9340, 1557-9956
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!