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Architectural Patterns for Designing Quantum Artificial Intelligence Systems

Mykhailo Klymenko, Thong Hoang, Xiwei Xu, Zhenchang Xing, Muhammad Usman, Qinghua Lu, Liming Zhu

TL;DR

This systematic mapping identifies architectural patterns for quantum-enhanced AI systems, addressing the challenge of translating quantum algorithms into deployable, high-quality software. It catalogs ten patterns (seven quantum-classical split and three middleware) within a reference architecture, detailing how quantum and classical components interact to balance efficiency, scalability, trainability, and deployability. While evidence of practical quantum advantage remains limited, the study outlines potential gains and prevalent design trajectories, including AutoQML, hardware-software co-design, and quantum DevOps. The findings offer a structured pattern catalogue to guide architects in designing quantum AI applications suited to current NISQ hardware and evolving quantum infrastructure, with an eye toward scalable, maintainable systems.

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.

Architectural Patterns for Designing Quantum Artificial Intelligence Systems

TL;DR

This systematic mapping identifies architectural patterns for quantum-enhanced AI systems, addressing the challenge of translating quantum algorithms into deployable, high-quality software. It catalogs ten patterns (seven quantum-classical split and three middleware) within a reference architecture, detailing how quantum and classical components interact to balance efficiency, scalability, trainability, and deployability. While evidence of practical quantum advantage remains limited, the study outlines potential gains and prevalent design trajectories, including AutoQML, hardware-software co-design, and quantum DevOps. The findings offer a structured pattern catalogue to guide architects in designing quantum AI applications suited to current NISQ hardware and evolving quantum infrastructure, with an eye toward scalable, maintainable systems.

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.

Paper Structure

This paper contains 10 sections, 1 equation, 11 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Research Methodology
  3. Reference architecture, role of architectural patterns in quantum AI and related works
  4. Pattern catalog for quantum AI systems (RQ1)
  5. Quantum-classic split architectural patterns (SP)
  6. Quantum middleware architectural patterns (MP)
  7. Rationale for using quantum computing in AI systems (RQ2)
  8. Discussion
  9. Threats to validity
  10. Conclusions

Figures (11)

  • Figure 1: An overall framework for conducting a systematic mapping study
  • Figure 2: Reference architecture for quantum-enhanced AI systems, featuring data pre-processing (Data PP), classical components (CC), and quantum components (QC), annotated with relevant architectural patterns.
  • Figure 3: Quantum circuits used in quantum machine learning applications: a) variational quantum circuit, b) variational quantum circuit implementing data re-uploading KMRPKRUT c) quantum kernel of the support vector machine QJA5MGMD, d) quantum convolutional neural network NLHULMET, and e) deep quantum neural network Beer2020beer2021training.
  • Figure 4: Quantum monolith
  • Figure 5: Multi-layer
  • ...and 6 more figures