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数字RGB图像的混合量子位表示。

A hybrid-qudit representation of digital RGB images.

作者信息

Das Sreetama, Caruso Filippo

机构信息

Department of Physics and Astronomy, University of Florence, Via Sansone 1, Sesto Fiorentino, 50019, Italy.

European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Via Nello Carrara 1, Sesto Fiorentino, 50019, Italy.

出版信息

Sci Rep. 2023 Aug 22;13(1):13671. doi: 10.1038/s41598-023-39906-9.

Abstract

Quantum image processing is an emerging topic in the field of quantum information and technology. In this paper, we propose a new quantum image representation of RGB images with deterministic image retrieval, which is an improvement over all the similar existing representations in terms of using minimum resource. We use two entangled quantum registers constituting of total 7 qutrits to encode the color channels and their intensities. Additionally, we generalize the existing encoding methods by using both qubits and qutrits to encode the pixel positions of a rectangular image. This hybrid-qudit approach aligns well with the current progress of NISQ devices in incorporating higher dimensional quantum systems than qubits. We then describe the image encoding method using higher-order qubit-qutrit gates, and demonstrate the decomposition of these gates in terms of simpler elementary gates. We use the Google Cirq's quantum simulator to verify the image preparation in both the ideal noise-free scenario and in presence of realistic noise modelling. We show that the complexity of the image encoding process is linear in the number of pixels. Lastly, we discuss the image compression and some basic RGB image processing protocols using our representation.

摘要

量子图像处理是量子信息与技术领域中一个新兴的课题。在本文中,我们提出了一种具有确定性图像检索功能的RGB图像的新型量子图像表示方法,就资源使用最少而言,它是对所有类似现有表示方法的一种改进。我们使用由总共7个三量子比特组成的两个纠缠量子寄存器来编码颜色通道及其强度。此外,我们通过使用量子比特和三量子比特来编码矩形图像的像素位置,对现有的编码方法进行了推广。这种混合量子位方法与含噪声中等规模量子(NISQ)设备在纳入比量子比特更高维量子系统方面的当前进展非常契合。然后,我们描述了使用高阶量子比特 - 三量子比特门的图像编码方法,并展示了这些门如何分解为更简单的基本门。我们使用谷歌Cirq的量子模拟器在理想无噪声场景以及存在实际噪声模型的情况下验证图像制备过程。我们表明图像编码过程的复杂度与像素数量呈线性关系。最后,我们使用我们的表示方法讨论图像压缩和一些基本的RGB图像处理协议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d586/10444894/958c9fc3e5ec/41598_2023_39906_Fig1_HTML.jpg

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