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IonQ, Oak Ridge National Laboratory, NVIDIA and the University of Tennessee, Knoxville have published joint work showing that a generative AI model can write quantum optimisation circuits directly, skipping the tuning loops that today make hybrid quantum-classical algorithms slow and finicky.
DQAOA-GPT Replaces The Tuning Loop
The method — called DQAOA-GPT (distributed quantum approximate optimisation algorithm, GPT) — is a transformer trained on near-optimal circuits produced by conventional QAOA runs. Given a new subproblem, it samples ten candidate circuits and picks the highest-scoring one, in place of the usual iterative parameter search.
On a 100-variable benchmark, the researchers report that solution quality "roughly doubled" as subproblem size grew, while the time to generate a circuit stayed nearly flat at about 28 seconds. Traditional tuning, by contrast, blew up from 34 seconds to more than 11 minutes on the same range.
Built On NVIDIA cuQuantum And CUDA-Q
All circuits were simulated with the NVIDIA cuQuantum SDK via CUDA-Q on an NVIDIA H200 GPU inside Oak Ridge's Defiant2 system at the Oak Ridge Leadership Computing Facility. The stack pairs classical GPU acceleration with the trapped-ion hardware roadmap IonQ has been publishing since it debuted Superion 256 earlier this month.
Executives Frame It As A Shortcut To Useful Quantum
"Better answers…have traditionally come with a steep tuning tax. In this benchmark, generative AI replaced the iterative tuning loop, and as the quantum subproblems grew the solution quality improved," said Dr. Martin Roetteler, IonQ's vice president for quantum applications R&D. NVIDIA's director of quantum product, Sam Stanwyck, added that "drawing on accelerated computing and AI to make breakthroughs in quantum algorithms is one of the most promising ways to reach useful quantum applications quickly."
Stocks React
Public quantum peers moved on the release, with IonQ up roughly 9% on September 17 and D-Wave and Rigetti climbing 8% and 7% respectively, according to trading coverage. The research adds another data point to the AI-plus-quantum thesis that has also driven work like IonQ's earlier NVIDIA and qBraid collaboration on mid-circuit error correction and NVIDIA's CUDA-Q Logical stack for fault-tolerant quantum.
Reporting based on coverage from IonQ, Yahoo Finance, 24/7 Wall St. and NVIDIA.