Work

Utility of Quantum Kernel Machines in Remote Sensing Applications

Year: 2024

Type: conference-paper

Abstract: We investigate the runtime of quantum kernel estimation in the view of quantum kernel concentration effect. The study is performed for projected quantum kernel family evaluated on hyperspectral remote sensing data. The effect of exponential value concentration leads to the indistinguishability of the kernel matrix entries as the size of the quantum device grows. In order to prevent that, kernel values have to be estimated with a better precision. Increasing precision inevitably connects to an increasing number of circuit runs, which influences the runtime of quantum algorithm. This, in turn, frequently obstructs a possible advantage for quantum machine learning methods. We find that, against popular opinions, the effect of exponential value concentration does not rule out the utility of quantum kernel methods and the severity of the issue depends highly on the data used.

Source: IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

DOI: 10.1109/igarss53475.2024.10640823

Authors: Artur Miroszewski, Bertrand Le Saux, Nicolas Longépé, Jakub Nalepa

Institutions: Jagiellonian University, European Space Agency, European Space Research Institute

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Space Agency

OpenAlex ID: W4402262135

FWCI: 0.577

Cites: 26

Cited by: 2

Related to: 10