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This study establishes a granular performance model for hybrid workflows by decomposing costs into quantum compute, classical compute, and communication overheads. The findings indicate that co-locating quantum processors with HPC infrastructure yields negligible gains for most compute-intensive tasks but remains critical for real-time applications like error correction where latency is paramount.
How NVIDIA & Amazon Model Hybrid Quantum-Classical Workflow Performance

NVIDIA & Amazon researchers detail a performance model for integrating quantum computing with high-performance computing, analyzing how communication impacts runtime.
Researchers from NVIDIA Corporation and Amazon Braket collaborated on this work to address a critical gap in the emerging field of hybrid quantum-classical computing by creating a unified framework for performance analysis. The work decomposes workflow execution into three key cost categories: quantum compute, classical compute, and communication costs, moving beyond simple runtime measurements to offer a granular understanding of bottlenecks. At the application level, a communication-to-computation ratio derived from this decomposition quantifies whether a workflow is communication-bound or compute-bound. Application of this model demonstrates that, for compute-intensive tasks, co-location of quantum processors with existing high-performance…
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