Work

Encoding hyperspectral data with low-bond dimension quantum tensor networks

Year: 2025

Type: conference-paper

Abstract: Encoding data on a quantum computer poses a major challenge on data intensive quantum applications like machine learning.In particular, data with complex internal structure like emission spectra need to be adapted to reduce the encoding effort of quantum circuits.We empirically investigate the influence of compression on the encoding of hyperspectral data into quantum states, to make its encoding more efficient.To this end, we assess the effect of approximating states by low-bond dimension matrix product states fed into a variational quantum classifier on the public Pavia University benchmark dataset.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-91

Authors: Fabian Fischbach, Hans-Martin Rieser, Oliver Sefrin

Institution: Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409480783

FWCI: 2.118

Cites:

Cited by: 2

Related to: 10