A 31-fold gain in design-space exploration let ITIS Software researchers evaluate hybrid quantum-classical system configurations that earlier methods could not tractably assess.
The method formalizes a service-oriented architecture for linking quantum and classical components, then selects the best setup using measurable quality-of-service targets such as execution time, resource use and accuracy.
NISQ hardware constraints—limited qubits, high error rates and machine-specific algorithms—make that systematic approach critical because some workloads still run better and more cheaply on purely classical systems.
By abstracting hardware details and enabling dynamic configuration changes, the framework shifts quantum software work from isolated algorithm demos toward deployable, adaptable applications and points to machine-learning-based optimization as a next step.