DVS - Distributed Viability Score
Variational Quantum Algorithms (VQAs) are widely seen as one of the most practical methods for near-term quantum computing, but their scalability remains limited by qubit availability, noise, transpilation overhead, and sampling cost. Distributed quantum computing and circuit cutting aim to extend execution across multi-QPU architectures and quantum networks. However, distribution is not inherently advantageous, as it depends on problem structure, ansatz and mixer locality, partitioning strategy, and communication overhead. This paper proposes a network-sensitive framework for Distributed VQAs (D-VQAs) that combines hypergraph partitioning, ansatz-aware and mixer-aware strategies, and a cost perspective including local execution, communication, synchronization, sampling, and reconstruction. The framework identifies viability regimes in which distribution may provide architectural advantage or be dominated by network overhead.
A dataset with benchmark quantum curcuits created for this research is published at IEEE DATAPORT, as follows:
dataset at IEEE DataPort
Waldemir Cambiucci, "A Network-Sensitive Framework for Variational Quantum Optimization on Multi-QPU Architectures", IEEE Dataport, March 15, 2026, doi:10.21227/s9b5-qy94 https://ieee-dataport.org/documents/network-sensitive-framework-variational-quantum-optimization-multi-qpu-architectures
To support the exploratory analysis for those benchmark circuits, we created scripts for different tasks, as follows here. Scripts documentation