"A Network-Sensitive Framework for Variational Quantum Optimization on Multi-QPU Architectures"

"Variational Quantum Algorithms (VQAs) have emerged as one of the most promising approaches for near-term quantum computing because they combine parametrized quantum circuits with classical optimization and can be adapted to optimization, chemistry, and machine learning tasks. Despite this potential, their practical scalability remains constrained by limited qubit counts, hardware noise, circuit depth, transpilation overhead, and the high sampling cost required to estimate observables during iterative optimization. One possible path to extend the executable scale of VQAs is to distribute circuits across multiple Quantum Processing Units (QPUs), either through distributed quantum computing architectures connected by quantum networks or through circuit cutting techniques that decompose larger circuits into smaller subcircuits. Yet distribution is not automatically beneficial: the gains obtained from larger effective circuit capacity may be offset by communication latency, synchronization penalties, statistical reconstruction overhead, and degradation of variational performance when critical circuit regions are fragmented.This dataset is part of a paper exploring a network-sensitive framework for analyzing Distributed VQAs (D-VQAs) as an architectural decision rather than a purely mechanical circuit transformation. The framework is structured around five stages: optimization problem formulation, variational circuit design, structural representation for partitioning, execution in a multi-QPU environment, and explicit cost evaluation. In this view, the viability of distribution depends on the interaction among four central factors: the modularity and global coupling structure of the optimization problem, the locality properties of the ansatz and mixer, the quality of the structural partitioning, and the overheads induced by the multi-QPU environment. To model these dependencies, the framework combines hypergraph-based circuit representation with partitioning strategies that are not only structure-aware, but also ansatz-aware and mixer-aware, seeking to preserve expressive variational blocks and local entanglement patterns while reducing critical cuts and inter-partition communication.By making these artifacts available through IEEE DataPort, this work contributes a reusable basis for benchmarking, reproducibility, and future research on the architectural feasibility of variational quantum optimization in multi-QPU settings."