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A case study in autonomous migration of the QED-C Application-Oriented Benchmarks from Qiskit to CUDA-Q results in publicly available, near-complete CUDA-Q support in the QED-C benchmarks along with model-generated skills designed to improve agent performance when implementing CUDA-Q applications.
The results show that circuit-level parallelism can reduce execution cost on current quantum hardware and simulation time on GPU clusters, with the potential for greater benefits as device quality and qubit counts increase.
This work develops a methodology for fair benchmarking of quantum and classical machine learning models, based on the Red Cedar quantum machine learning and resource estimation framework and AutoML-optimized classical neural networks, and finds no evidence of quantum advantage.
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