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 📷 ↧ IonQ Generative AI Accelerates Quantum Optimization IonQ & NVIDIA IonQ, ORNL, and NVIDIA reveal how generative AI synthesizes quantum circuits directly—eliminating costly parameter tuning to deliver constant runtimes at scale.