AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A practical baseline for VQLS in hybrid quantum--HPC computation fluid dynamic (CFD) workflows is established and the remaining bottlenecks for larger problems are identified.
This paper introduces S ELE Q TOR, which develops meta-backend-driven benchmarking, a technique that leverages workload-sensitive metrics to select the best backend per workload and provide runtime estimates for intelligent Q-HPC resource scheduling.
DQAOA-GPT is introduced, a hybrid framework that integrates the distributed quantum approximate optimization algorithm (DQAOA), which decomposes a large optimization problem into smaller sub-problems, with GPT-based quantum circuit generation for solving those sub-problems.
A variational quantum optimization algorithm (VQOA), which employs an ansatz based solely on quantum superposition, where single-qubit rotation gates function analogously to neurons in classical deep neural networks, achieving more than 50 × speedup compared to state-of-the-art optimization algorithms.
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