Determining links in product data using Quantum Restricted Boltzmann Machines

Determining links in product data using Quantum Restricted Boltzmann Machines

Abstract:

The increasing share of software in products not only drives innovation but also increases complexity. To minimize the risk of malfunctions in software-intensive products and ensure traceability, links between development stages and production data are necessary. These are mandated by regulations such as ISO/IEC 15288 and DIN/ISO 26262. The Digital Data Package standard enables the management of such links. However, implicit links currently have to be created manually, which leads to challenges due to the scope and frequent product changes. A promising approach for the automatic identification of links is the use of classifiers. In particular, Quantum Restricted Boltzmann Machines offer a viable solution due to the limited availability of linked development data and their high susceptibility to noise. For evaluation, classical neural networks and pre-trained classifiers are used. As established methods in pattern recognition, they serve as a baseline for assessing new classifiers.

Author:

Simon Hehnen

Advisors:

Michael Kölle, Jonas Stein, Dr. Fabrice Mogo Nem (PROSTEP AG), Claudia Linnhoff-Popien

Student Thesis | Published April 2025 | Copyright © QAR-Lab
Direct Inquiries to this work to the Advisors