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#