IonQ and ORNL Demonstrate Generative AI for Quantum Optimization

Insider Brief

• IonQ, ORNL, NVIDIA and the University of Tennessee, Knoxville demonstrated a generative AI method that directly generates quantum optimization circuits, replacing iterative parameter tuning.
• In benchmark experiments, the generative approach maintained circuit-generation time at about 28 seconds across tested problem sizes, while the prior method increased from about 34 seconds on four qubits to more than 11 minutes on 12 qubits.
• The study used NVIDIA H200 GPU simulation rather than quantum hardware and found that model-generated solution quality roughly doubled as subproblem size increased on a 100-variable benchmark.

PRESS RELEASE — IonQ (NYSE: IONQ), the world’s leading full-stack quantum platform and foundry, today detailed joint research with Oak Ridge National Laboratory (ORNL), NVIDIA, and the University of Tennessee, Knoxville (UT) showing that a trained generative model can write quantum optimization circuits directly, eliminating the trial-and-error parameter-tuning loop that has made the most accurate approach too costly to run. The paper is being presented this week at IEEE Quantum Week in Toronto.

Hybrid quantum optimization breaks a large problem into smaller pieces, solves each one, and recombines the results. Each piece needs a tailored quantum circuit, which traditionally required trial- and-error parameter tuning: run, measure, adjust, and repeat, often hundreds of times. Larger pieces can improve answers, but they also raise tuning costs, limiting the size of problems researchers could solve.