# Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations

arXiv: https://arxiv.org/abs/2212.01312 · HTML: https://mlsanity.com/paper/2212.01312/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 59 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [Nonnegative/binary matrix factorization with a D-Wave quantum annealer](https://mlsanity.com/paper/1704.01605/) (2017)
- [Fast clique minor generation in Chimera qubit connectivity graphs](https://mlsanity.com/paper/1507.04774/) (2015)
- [Comparison of D-Wave Quantum Annealing and Classical Simulated Annealing for Local Minima Determination](https://mlsanity.com/paper/1911.03338/) (2019)
- [Quantum Medical Imaging Algorithms](https://mlsanity.com/paper/2004.02036/) (2020)
- [Highly accurate quantum optimization algorithm for CT image reconstructions based on sinogram patterns](https://mlsanity.com/paper/2207.02448/) (2022)

## Built on by (most cited first, 2 of 2)

- [Quantum Annealing Feature Selection on Light-weight Medical Image Datasets](https://mlsanity.com/paper/2502.19201/) (2025)
- [Quantum CT via Dynamic Interval Encoding and Prior-Balanced QUBO Reconstruction](https://mlsanity.com/paper/2606.24561/) (2026)

## Abstract (arXiv)

Our goal is to reconstruct tomographic images with few measurements and a low signal-to-noise ratio. In clinical imaging, this helps to improve patient comfort and reduce radiation exposure. As quantum computing advances, we propose to use an adiabatic quantum computer and associated hybrid methods to solve the reconstruction problem. Tomographic reconstruction is an ill-posed inverse problem. We test our reconstruction technique for image size, noise content, and underdetermination of the measured projection data. We then present the reconstructed binary and integer-valued images of up to 32 by 32 pixels. The demonstrated method competes with traditional reconstruction algorithms and is superior in terms of robustness to noise and reconstructions from few projections. We postulate that hybrid quantum computing will soon reach maturity for real applications in tomographic reconstruction. Finally, we point out the current limitations regarding the problem size and interpretability of the algorithm.