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.

翻译：我们的目标是用很少的测量和低信号到噪音比率来重建成像图像。 在临床成像中,这有助于提高病人的舒适度和减少辐射照射。 随着量子计算的进步,我们提议使用一种半闭音量计算机和相关的混合方法来解决重建问题。 成像重建是一个错误的反向问题。 我们测试我们的重建技术,以图像大小、噪音内容和测量的投影数据的确定程度为基础。 然后,我们提出重建的二进制和整值图像,最高为32比素。 所展示的方法与传统的重建算法相竞争,并且比少数预测的噪声和重建强。 我们假设混合量计算将很快成熟,在成像学重建中的真正应用。 最后,我们指出目前对算法问题大小和可解释性的限制。