Quantum Supremacy in Tomographic Imaging: Advances in Quantum Tomography Algorithms

TL;DR

This paper demonstrates that quantum algorithms can significantly improve tomographic image reconstruction by reducing projection angles and increasing robustness, achieving artifact-free images even with high error rates.

Contribution

It introduces novel quantum tomography algorithms that outperform classical methods in efficiency and accuracy, especially under noisy conditions.

Findings

Accurate reconstructions with 50% fewer projection angles.

Robustness against 50% sinogram errors.

Artifact-free images despite high error rates.

Abstract

Quantum computing has emerged as a transformative paradigm, capable of tackling complex computational problems that are infeasible for classical methods within a practical timeframe. At the core of this advancement lies the concept of quantum supremacy, which signifies the ability of quantum processors to surpass classical systems in specific tasks. In the context of tomographic image reconstruction, quantum optimization algorithms enable faster processing and clearer imaging than conventional methods. This study further substantiates quantum supremacy by reducing the required projection angles for tomographic reconstruction while enhancing robustness against image artifacts. Notably, our experiments demonstrated that the proposed algorithm accurately reconstructed tomographic images without artifacts, even when up to 50% error was introduced into the sinogram to induce ring artifacts. Furthermore, it achieved precise reconstructions using only 50% of the projection angles from the original sinogram spanning 0{\deg} to 180{\deg}. These findings highlight the potential of quantum algorithms to revolutionize tomographic imaging by enabling efficient and accurate reconstructions under challenging conditions, paving the way for broader applications in medical imaging, material science, and advanced tomography systems as quantum computing technologies continue to advance.