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Towards model-driven engineering for quantum AI

Moin, A., Challenger, M., Badii, A. and Günnemann, S. (2022) Towards model-driven engineering for quantum AI. INFORMATIK 2022, Lecture Notes in Informatics (LNI). pp. 1121-1131. ISSN 1617-5468

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To link to this item DOI: 10.18420/inf2022_95


Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. In the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC), with perhaps a quantum-classical hybrid model, is expected. We argue that the Model-Driven Engineering (MDE) paradigm can be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, particularly Quantum ML for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) applications.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:108663
Publisher:Gesellschaft für Informatik


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