IonQ, ORNL, NVIDIA, and the University of Tennessee, Knoxville Show AI Method Reduces Quantum Optimization Trade Off
IonQ, Oak Ridge National Laboratory, NVIDIA, and the University of Tennessee, Knoxville published joint research showing a trained generative AI model can write quantum optimization circuits directly, eliminating the trial-and-error parameter-tuning loop that made the most accurate hybrid approach costly. The paper, presented at IEEE Quantum Week in Toronto, won a best paper award and is one of nine IonQ papers accepted at the conference.
Key figures
- Generative runtime
- nearly 28 seconds constant across tested sizes
- Answer quality change
- roughly doubled as subproblems grew
- Two qubit gate fidelity
- 99.99% (2025 world record, per boilerplate)
- Baseline runtime 4 qubits
- about 34 seconds
- Baseline runtime 12 qubits
- more than 11 minutes
- Benchmark decision variables
- 100
- Ionq papers ieee quantum week
- 9
- Candidate circuits per subproblem
- 10
AI analysis
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