QSplit: A Workflow-Oriented Hybrid Quantum–Classical Optimization Framework

Current quantum processors are too limited to solve large combinatorial optimization problems directly, motivating hybrid executions that combine quantum processors with classical HPC resources. This paper presents QSplit, a workflow-oriented hybrid quantum-classical framework for distributed optimization. QSplit targets problems expressed in the Quadratic Unconstrained Binary Optimization form, decomposes large instances into smaller sub-instances, dispatches them to heterogeneous classical or quantum backends, and reconstructs a global solution within a portable and reproducible workflow. The orchestration layer of the QSplit workflow is managed by StreamFlow WMS, enabling configurable execution across cloud, HPC, and remote quantum infrastructures. We evaluate QSplit on Knapsack and Max-Cut instances to study how problem structure affects decomposition quality. Results show that QSplit achieves competitive behavior on sparse instances, where decomposition preserves enough structure for effective aggregation, while dense instances expose the limits of purely structural splitting due to the loss of global correlations. These findings suggest that QSplit provides a framework for studying hybrid HPC-quantum optimization pipelines and their current scalability boundaries.