Quantum-native formulations for Computed Tomography: reconstruction, denoising and QUBO-based segmentation

Computed Tomography (CT) is fundamentally an inverse problem combining linear operators, regularization and discrete inference. Artificial intelligence has improved reconstruction, denoising and segmentation. Quantum Computing (QC) is typically discussed in terms of computational speed. For CT, the more relevant question is structural compatibility. Several CT subproblems admit formulations that are aligned with quantum-native primitives for structured linear algebra and quadratic optimization. This short paper identifies three directions: reconstruction, denoising and segmentation. It briefly formalizes each component and details a QUBO-based formulation for the segmentation stage implemented in a prototype demonstrator, QUBOSegment. We also report ongoing development of a production-oriented system, NextGenSegment (NGS), built on our Modular Adaptive Processing Infrastructure (MAPI). This work is intentionally scoped as a formulation- and workflow-oriented proof of concept rather than a benchmarking study claiming quantum performance advantage. The aim is to expose the CT community to technically grounded QC formulations and to encourage systematic benchmarking in realistic synchrotron and laboratory settings.