论文标题
层合图像重建的混合绝热量子计算 - 机会和局限性
Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations
论文作者
论文摘要
我们的目标是重建几乎没有测量的层析成像图像和低信噪比。在临床成像中，这有助于改善患者舒适度并减少辐射暴露。随着量子计算的进步，我们建议使用绝热量子计算机和相关的混合方法来解决重建问题。层析成像重建是一个不良的反问题。我们测试了测量投影数据的图像大小，噪声含量和不确定的重建技术。然后，我们介绍了高达32 x 32像素的重建的二进制和整数值图像。演示的方法与传统的重建算法竞争，并且在稳健性方面优于噪声和几个预测的重建。我们假设，混合量子计算很快将在层析成像重建中的实际应用到期。最后，我们指出有关算法的问题大小和解释性的当前局限性。
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.
