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Patterns for Quantum Machine Learning

Authors:
Lavinia Stiliadou
Johanna Barzen
Martin Beisel
Frank Leymann
Benjamin Weder

Keywords: Quantum Computing, Pattern Language, QML

Abstract:
The tremendous success of applying machine learning techniques in science and industry leads to new challenges: Developing new, faster, or more precise algorithms can not compete with the continuously growing data volumes to process. Thus, the computational resources must be increased, leading to high costs and energy consumption. To overcome these issues, quantum machine learning promises to utilize quantum mechanical phenomena to train machine learning models more efficiently. However, realizing such quantum machine learning techniques is time-consuming and complex. This is especially the case as new techniques are typically published as scientific papers without suitable documentation for software developers. Patterns are a well-established concept to document proven solutions to recurring problems. Although a pattern language for quantum computing has been introduced, it currently misses patterns documenting how quantum machine learning can be successfully applied. To bridge this gap, this paper presents three novel patterns, focusing on quantum machine learning techniques.

Pages: 7 to 14

Copyright: Copyright (c) IARIA, 2025

Publication date: April 6, 2025

Published in: conference

ISSN: 2308-3557

ISBN: 978-1-68558-263-0

Location: Valencia, Spain

Dates: from April 6, 2025 to April 10, 2025