Exploring the Limitations of Hybrid Adiabatic Quantum Computing for Emission Tomography Reconstruction

Our study explores the feasibility of quantum computing in emission tomography reconstruction, addressing a noisy ill-conditioned inverse problem. In current clinical practice, this is typically solved by iterative methods minimizing a L2 norm. After reviewing quantum computing principles, we propose the use of a commercially available quantum annealer and employ corresponding hybrid solvers, which combine quantum and classical computing to handle more significant problems. We demonstrate how to frame image reconstruction as a combinatorial optimization problem suited for these quantum annealers and hybrid systems. Using a toy problem, we analyze reconstructions of binary and integer-valued images with respect to their image size and compare them to conventional methods. Additionally, we test our method's performance under noise and data underdetermination. In summary, our method demonstrates competitive performance with traditional algorithms for binary images up to an image size of 32x32 on the toy problem, even under noisy and underdetermined conditions. However, scalability challenges emerge as image size and pixel bit range increase, restricting hybrid quantum computing as a practical tool for emission tomography reconstruction until significant advancements are made to address this issue.

Automated summary

Our study explores the feasibility of using quantum computing in emission tomography reconstruction, and proposes the use of a commercially available quantum annealer. We compare the performance of quantum computing with traditional algorithms and find that quantum computing shows competitive performance for small binary images, even under noisy and underdetermined conditions. However, scalability challenges exist as image size and pixel bit range increase.