The Need for Quantitative Resilience Models and Metrics in Classical-Quantum Computing Systems

TL;DR

This paper emphasizes the importance of developing quantitative resilience models for hybrid classical-quantum computing systems to improve dependability, security, and performance assessment.

Contribution

It advocates for integrating resilience as a fundamental design constraint and applying civil engineering resilience methods to classical-quantum computing architectures.

Findings

Resilience should be an  priori design constraint.

Applying civil engineering resilience methods to quantum systems is beneficial.

Quantitative models can clarify the value of system component improvements.

Abstract

Increasingly deeper integration of HPC resources and QPUs unveils new challenges in computer architecture and engineering. As a consequence, dependability arises again as a concern encompassing resilience, reproducibility and security. The properties of quantum computing systems involve a reinterpretation of these factors in retrodictive, predictive, and prescriptive ways. We state here that resilience must become an \emph{a priori} design constraint rather than an afterthought of HPC-QPU integration. This article describes the need for conceptual and quantitative models to estimate and assess the resilience hybrid classical-quantum computing infrastructure. We suggest how resilience methods in civil engineering can apply at various levels of the classical-quantum computing stack. We also discuss implications of a model of end-user value for the estimation of consequences resulting from the propagation of vulnerabilities from a given level of the stack upwards. Finally, we argue in favor of new resilience models can help the impact of improving specific components in quantum technology stacks to provide a clearer picture about the value of separation of concerns across different layers. Ultimately, HPC-QPU integration will increasingly demand more precise statements about the cost-benefit ratio of specific system improvements and their cascading consequences against estimates of delivered value to users.