IonQ, ORNL, NVIDIA, and the University of Tennessee, Knoxville Show AI Method Reduces Quantum Optimization Trade Off

An AI model directly generated quantum circuits, making accurate large-scale optimization faster and more affordable with constant runtime as problems grow

TORONTO & COLLEGE PARK, Md.--(BUSINESS WIRE)--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.

“Better answers in hybrid quantum optimization have traditionally come with a steep tuning tax. In this benchmark, generative AI replaced the iterative tuning loop, and as the quantum subproblems grew the solution quality improved,” said Dr. Martin Roetteler, IonQ’s Vice President of Quantum Applications R&D and a co-author of the paper. “Take that cost away and you can work at the size where the answer is meaningful. The result provides a potential path toward scaling hybrid quantum optimization, unlocking completely new capabilities and scales that align with IonQ’s existing and future quantum computing hardware generations.”

To teach the generative model how to tune circuit instructions, the team first showed it what strong results looked like. They ran the conventional trial-and-error method across many sampled problems, kept only the near-optimal circuits, and used those examples to train a transformer, the same class of model behind large language models but trained on circuits instead of text.

The trained model generates candidate quantum circuits directly, without the repetitive parameter-tuning loop used by conventional methods. In the reported experiments, the model sampled ten candidate circuits for each subproblem; all ten were simulated and scored, and the best-scoring candidate was used to update the global solution.

On a dense, higher-order benchmark problem with 100 decision variables, model-generated answer quality roughly doubled as subproblems grew. Under the prior state-of-the-art method, circuit-finding time rose sharply from about 34 seconds on 4 qubits to more than 11 minutes on 12 qubits. The generative approach held nearly 28 seconds for every size previously tested. Both approaches are quantum methods; the study compares circuit-generation approaches, not quantum against classical solvers.

ORNL led the study. Co-authors span ORNL's National Center for Computational Sciences and its Materials Science and Technology Division, IonQ, NVIDIA, and UT. Abhinav Rijal, a graduate researcher in the Department of Physics and Astronomy at UT is a co-author of the study.

“This work brings generative AI, quantum computing, and high-performance computing together to tackle large-scale, complex optimization problems. AI can become a new computational layer for quantum circuit synthesis, enabling the automatic design and optimization of quantum circuits for increasingly complex problems. We are now extending the framework to real-world scientific and engineering applications and scaling it across larger HPC systems to address problems of even greater scale and complexity,” said Dr. In-Saeng Suh and Dr. Seongmin Kim, National Center for Computational Sciences, ORNL.

“Drawing on accelerated computing and AI to make breakthroughs in quantum algorithms is one of the most promising ways to reach useful quantum applications as quickly as possible,” said Sam Stanwyck, Director, Quantum Product at NVIDIA. “By enabling developers to build quantum algorithms architected around AI from the outset, tools like CUDA-Q are laying the foundation for the next generation of advances in quantum computing and its useful application.”

The paper describes its results as benchmark-scale validation.

Every circuit in the study was simulated rather than executed on quantum hardware, using the NVIDIA cuQuantum SDK through the NVIDIA CUDA-Q open platform for quantum-GPU supercomputing on a single NVIDIA H200 GPU in the Oak Ridge Leadership Computing Facility’s Defiant2 system. That environment is what makes the comparison meaningful: both the trial-and-error method and the generative one ran on identical GPU-accelerated infrastructure, providing a controlled comparison of the two end-to-end workflows. The measured difference principally reflects the distributed quantum approximate optimization algorithm (DQAOA)-GPT’s replacement of iterative variational parameter optimization with generative circuit synthesis and a fixed number of candidate evaluations.

The paper is available at arXiv:2607.20225. It is one of nine IonQ papers accepted at IEEE Quantum Week 2026, held Sept. 13–18 at the Metro Toronto Convention Centre, and the winner of a best paper award.

About IonQ

IonQ, Inc. [NYSE: IONQ] is the world’s leading quantum platform and foundry - delivering integrated quantum solutions across computing, networking, sensing, and security. IonQ’s newest generation of quantum computers, the Superion, is the latest in a line of cutting-edge systems. Earlier systems have helped customers and partners including Amazon Web Services, AstraZeneca, and NVIDIA achieve a 20x performance increase over previous quantum solutions and accelerate innovation in drug discovery, materials science, financial modeling, logistics, cybersecurity, and defense. In 2025, the company achieved 99.99% two-qubit gate fidelity, setting a world record in quantum computing performance.

Headquartered in College Park, Maryland, IonQ has operations across North America, Latin America, EMEA, and APAC. Our quantum computing services have been available through all major cloud providers since 2021, while we also meet the needs of networking and sensing customers across land, sea, air, and space. IonQ is making quantum platforms more accessible and impactful than ever before. Learn more at IonQ.com.

About ORNL

UT-Battelle manages ORNL for DOE’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time. For more information, visit energy.gov/science.

About UT

The University of Tennessee, Knoxville, is Tennessee's flagship land-grant university and is home to more than 40,000 students across 14 degree-granting colleges and schools. UT holds the highest Carnegie classification for research activity and maintains deep partnerships with industry and the U.S. Department of Energy's Oak Ridge National Laboratory. The university is expanding research and education in quantum science and technology through collaborations spanning quantum computing, networking, sensing and materials.

Note to Investors Regarding Forward-Looking Statements

This press release contains forward-looking statements. All statements contained in this press release other than statements of historical fact are forward-looking statements, including statements regarding the potential applicability, scalability, and performance of the DQAOA-GPT framework; the expected benefits of combining artificial intelligence, high-performance computing, and quantum computing; the anticipated ability to reduce runtimes through parallel execution across larger computing systems; the role of artificial intelligence within the quantum computing stack as systems scale; the collaboration between IonQ and NVIDIA and its expected future output; the potential for these methods to be applied to larger-scale or real-world optimization problems; the future execution of these methods on quantum hardware; and IonQ’s continued research, development, and commercialization of quantum computing technologies. In some cases, you can identify these statements by forward-looking words such as such as “pending,” “look forward,” “accelerate,” “anticipate,” “expect,” “suggest,” “plan,” “believe,” “intend,” “estimate,” “target,” “project,” “should,” “could,” “would,” “may,” “will,” “forecast,” “confident,” “position,” “become,” “on track,” “ensure,” “ongoing” and other similar expressions. These statements are only predictions based on our expectations and projections about future events as of the date of this press release and are subject to a number of risks, uncertainties and assumptions that may prove incorrect, any of which could cause actual results to differ materially from those expressed or implied by such statements, including, among others, those described under the heading “Risk Factors” in our annual Report on Form 10-K for the year ended December 31, 2025 and our Quarterly Report on Form 10-Q for the quarter ended June 30, 2026 filed with the Securities and Exchange Commission. New risks emerge from time to time, and it is not possible for our management to predict all risks, nor can management assess the impact of all factors on our business or the extent to which any factor, or combination of factors, may cause actual results to differ materially from those contained in any forward-looking statement we make. Investors are cautioned not to place undue reliance on any such forward-looking statements, which speak only as of the date they are made. Except as otherwise required by law, we undertake no obligation to update any forward-looking statement, whether as a result of new information, future events or otherwise.

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