Volumetric Denoising of XCT Data Using Quantum Computing - Thomas Lang; Anja Heim; Anastasia Papadaki; Kilian Dremel; Dimitri Prjamkov; Martin Blaimer; Markus Firsching; Stefan Kasperl; Theobald O.J. Fuchs (e-Journal of Nondestructive Testing)
Quantum computing is an emerging field of technology that utilizes unique properties such as the superposition principle and quantum entanglement, potentially enabling the development of techniques that are drastically faster or even the only feasible solutions compared to classical approaches. Thus, it promises enormous gains in several domains, including the processing of n-dimensional images, such as those produced by X-ray computed tomography. However, the latter is subject to physical effects that primarily include artifacts and quantum noise. Noise, in particular, is an unavoidable issue that needs to be addressed when processing X-ray data. Therefore, this work considers the denoising of 3D volumetric data using quantum computing. An adaptation of a mathematical denoising model was transformed to be suitable for quantum implementation, and preliminary experiments demonstrated its functionality with two-dimensional images. Here, we first lift this model to be applicable to 3D images. Next, we provide an implementation in form of quantum circuits and consider issues of a practical implementation occurring specifically in 3D. The final implementation is executed on a real X-ray computed tomography dataset, showing that proper denoising can be performed on quantum devices, yet current technological limitations inhibit the application to large datasets.