Yenepoya Researchers Add a Four-Qubit Simulated Quantum Circuit to Chest X-Ray Tuberculosis Detection: Frontiers in AI, 11 November 2026

What the Yenepoya team built and tested on 662 Shenzhen chest X-rays

Quentir Medicine Monitor

Evidence-based insights for quantum medicine.

A research team in India has attached a four-qubit quantum circuit to six standard image-recognition networks and tested whether it helps detect pulmonary tuberculosis on chest X-rays. The top-scoring hybrid reached an area under the curve of 0.9686 on 662 public radiographs, slightly above its classical counterpart at 0.9668.

The study appeared in Frontiers in Artificial Intelligence on 11 November 2026. Its fourteen authors, led by first author Sonet Daniel Thomas with Rajesh Raju and Jalaluddin Akbar Kandel Codi as corresponding authors, work mainly at Yenepoya (Deemed to be University) in Mangalore, India, in its integrative omics data science unit and its medical college. The quantum circuit ran on a software simulator on ordinary processors, with no quantum hardware involved.

The most useful result for clinicians is a control experiment the authors built in themselves. They replaced the quantum circuit with a simple classical stand-in built from edge-detection texture features, kept the same feature-output dimensionality and the rest of the model, and retrained everything on the same data splits. That stand-in reached 0.9658, and the authors' paired statistical test found no significant difference in classification errors between it and the quantum hybrid.

In their comparative analysis the authors write that the improvement "should be regarded as incremental rather than definitive evidence of a quantum-specific advantage". For a hospital or a screening program weighing claims about quantum machine learning in radiology, that sentence is the part of the paper to keep.

The images come from the Shenzhen Hospital chest X-ray set, released by Jaeger and colleagues at the U.S. National Library of Medicine and described in Quantitative Imaging in Medicine and Surgery in 2014. It holds 662 frontal radiographs from Shenzhen No. 3 People's Hospital in China: 326 normal and 336 with tuberculosis. The near-even split makes the scores easier to read, though it does not match the low prevalence of a real screening population.

Each hybrid model has two branches. A classical network pre-trained on everyday photographs, one of VGG16, ResNet50, DenseNet121, InceptionV3, EfficientNetB0 or EfficientNetV2-S, reads the full image at 256 by 256 pixels. In parallel, the same X-ray is shrunk to a grid of two by two pixels. Those four numbers become rotation angles on four simulated qubits, which are linked by three entangling gates and then measured. The few values from the quantum branch are joined to the hundreds from the classical network, and a small final layer gives the probability of tuberculosis.