IonQ Demonstrates Quantum Generative Modeling for High Resolution Radar Change Detection

Quantum models outperformed classical baselines on analyzing high-resolution SAR data, achieving this advantage on an IonQ Forte Enterprise system

COLLEGE PARK, Md. — September 24, 2026 — IonQ (NYSE: IONQ), the world's leading quantum full-stack platform and foundry, released research demonstrating how quantum generative models can improve change detection on complex high-resolution Synthetic Aperture Radar (SAR) and interferomic SAR (InSAR) data. Using real satellite sensor data, IonQ researchers compared the quantum approach with classical change-detection methods and tested it on IonQ hardware. The ability to detect ground changes from space has many applications including disaster response, infrastructure monitoring, defense, and land-use enforcement.  

"Satellites are exceptional at collecting imagery of the Earth. The value is in knowing what changed and whether it matters," said Jordan Shapiro, President, Quantum Platform at IonQ. "This research shows quantum generative models can take on a statistical challenge that becomes harder as radar imagery gets more granular. It's also a good example of what happens when our space and quantum teams work on the same problem."

SAR sees through clouds, fog, and darkness, which is why defense agencies, disaster responders, and infrastructure operators depend on it. Change detection analytics usually compare an estimate of what a scene should look like if nothing had changed with an actual image of the scene to determine the areas of change. With high-resolution SAR data, the available pixel statistics can become complex, making classical change detection less reliable. Conventional approaches often use additional preprocessing to make SAR easier to analyze, but at the cost of reducing fine spatial detail. 

To overcome this challenge, IonQ researchers tested a Quantum Circuit Born Machine (QCBM), a type of quantum generative model, to estimate what each scene's background should look like. The QCBM learns the joint statistical relationship between the before and after images. It then generates reference samples that help estimate the expected background and distinguish meaningful change from normal variation. 

The team ran the quantum model on IonQ’s own high-resolution SAR datasets. On challenging non-Gaussian SAR data from an airfield, a simulated quantum model scored higher on filtered F1 than both classical baselines tested. On a challenging non-Gaussian SAR test case from Marine Corps Air Station Miramar, the QCBM achieved a filtered F1 score of 0.41, compared with 0.24 and 0.16 for the two classical baselines evaluated. When preprocessing made the underlying pixel distributions approximately Gaussian, the performance gap largely disappeared. The results indicate that the quantum model's strongest relative performance occurred where conventional statistics were sparsest. The advantage remained when the simulation model was run on IonQ quantum hardware. 

InSAR adds even more complexity to the analysis task. This is a technique that uses phase differences across SAR images to measure surface deformation at millimeter-to-centimeter scale. In a separate InSAR experiment involving volcanic lava flows, the quantum and classical approaches achieved comparable peak performance. The results suggest quantum generative models may be especially useful when high-resolution sensing data produces sparse or difficult-to-model statistics. IonQ is building on these results to advance analytics in defense, critical infrastructure, and environmental monitoring.

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.

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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 our research findings and their future applications, including in defense, critical infrastructure and environmental monitoring. In some cases, you can identify these statements by forward-looking words 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 filed with the Securities and Exchange Commission, or SEC, and in our Quarterly Report on Form 10-Q for the quarter ended June 30, 2026 to be filed with the SEC. 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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