MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence

TL;DR

This paper explores how Model-Driven Engineering can support the development, verification, and analysis of Quantum AI systems, especially for Quantum Machine Learning in IoT and Cyber-Physical Systems.

Contribution

It proposes a vision for applying MDE techniques to Quantum AI, enabling automated code generation, model checking, and transformations for quantum-classical hybrid systems.

Findings

Identifies the potential of MDE in quantum computing applications

Highlights the importance of early-stage model analysis for Quantum ML

Suggests MDE can facilitate development of quantum-classical hybrid systems

Abstract

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.