Architectural Patterns for Designing Quantum Artificial Intelligence Systems

TL;DR

This paper systematically explores architectural patterns for integrating quantum computing into AI systems, addressing challenges and proposing solutions to improve software quality attributes like efficiency and scalability.

Contribution

It introduces a catalogue of architectural patterns for quantum-enhanced AI systems, highlighting how quantum components can be integrated and communicating effectively.

Findings

Identified patterns for quantum-classical integration in AI

Catalogued middleware solutions for communication

Analyzed trade-offs between software quality attributes

Abstract

Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversarial attacks, and reduce the number of parameters without compromising accuracy. However, moving beyond proof-of-concept or simulations to develop practical applications of these systems while ensuring high software quality faces significant challenges due to the limitations of quantum hardware and the underdeveloped knowledge base in software engineering for such systems. In this work, we have conducted a systematic mapping study to identify the challenges and solutions associated with the software architecture of quantum-enhanced artificial intelligence systems. The results of the systematic mapping study reveal several architectural patterns that describe how quantum components can be integrated into inference engines, as well as middleware patterns that facilitate communication between classical and quantum components. Each pattern realises a trade-off between various software quality attributes, such as efficiency, scalability, trainability, simplicity, portability, and deployability. The outcomes of this work have been compiled into a catalogue of architectural patterns.