IonQ and Capella Run a 20-Qubit Quantum Circuit Born Machine for SAR Change Detection on Forte Hardware: Filtered F1 0.32 Against 0.24 and 0.16 at MCAS Miramar, arXiv 2609.05313, 4 September 2026

On September 4, 2026, Samwel K. Sekwao, Shaunak De and colleagues at IonQ and Capella Space posted a paper training a 20-qubit quantum circuit Born machine to find what changed between two Capella radar images of Marine Corps Air Station Miramar, and on September 24 IonQ announced it. On IonQ's Forte hardware the model reached a filtered F1 of 0.32 against 0.24 and 0.16 for two classical baselines, while a Gaussianizing transform erased the gap. We read the SAR change detection result as dual-use machine learning for intelligence, surveillance and reconnaissance, weigh who gains from it given Capella's NRO Radar Commercial Augmentation award, and set out the per-scene retraining, the two-scene evidence base and the missing learned classical baseline that stand between this quantum machine learning for ISR proof of concept and an analytic a program office would rely on.