IonQ(@IonQ_Inc):The most accurate approach to training AI is too costly to run, due to trial-and-error parameter-tuning loops. Now, with partners @ORNL, @nvidia, and @UTKnoxville, we show that a trained generative model can write quantum optimization circuits directly—eliminating those costly loops. Hybrid quantum optimization breaks a large problem into smaller pieces, solves each one, and recombines the results for optimal results. Both the trial-and-error and generative methods ran through NVIDIA CUDA-Q, which provided a controlled comparison of the two end-to-end workflows. This research is being presented at #IEEEQuantumWeek in Toronto, where it won an award for best paper. Read the full announcement → https://t.co/ydM1eCPrmG #IonQ #QuantumIsNow #QuantumComputing #IEEE

2026.09.16 14:21

The most accurate approach to training AI is too costly to run, due to trial-and-error parameter-tuning loops. Now, with partners @ORNL, @nvidia, and @UTKnoxville, we show that a trained generative model can write quantum optimization circuits directly—eliminating those costly loops. Hybrid quantum optimization breaks a large problem into smaller pieces, solves each one, and recombines the results for optimal results. Both the trial-and-error and generative methods ran through NVIDIA CUDA-Q, which provided a controlled comparison of the two end-to-end workflows. This research is being presented at #IEEEQuantumWeek# in Toronto, where it won an award for best paper. Read the full announcement → #IonQ# #QuantumIsNow# #QuantumComputing# #IEEE#

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