QuBind: A Multi-Metric Approach for Optimal and Dynamic QPU Selection

Abstract

The emergence of Quantum Software Engineering (QSE) has introduced new challenges in the development and orchestration of hybrid applications that combine classical and quantum services. These applications are typically delivered through a Hybrid Software-as-a-Service (HSaaS) model, which integrates both computing paradigms within an unified cloud-based platform. One of the core problems in this domain is the orchestration of quantum services, particularly the selection of quantum processing units (QPUs) across one or multiple providers. Existing orchestration solutions rely on simplistic decision models based on a single metric. This reductionist approach fails to accommodate trade-offs between fidelity, cost, and execution time, which are often mutually conflicting. Furthermore, these models often overlook workload-specific requirements, such as user-defined priorities. This master’s thesis addresses this problem through the development of QuBind, a novel multi-metric QPU selection framework based on a matcher and optimizer pattern. The matcher filters candidate QPUs according to declarative feasibility constraints, while the optimizer ranks the feasible options based on configurable Figures of Merit. QuBind supports dynamic optimization across several performance and operational metrics, enabling context-aware decisions. It also subsumes existing approaches in the literature by reinterpreting and generalizing their orchestration strategies within our optimization framework.