Metrological comparison between a generalised N-dimensional classical and quantum point cloud

物联网（IoT）数据的大规模多模态传感器融合可以转换为N维经典点云。例如，变换可以是不同性质的三种成像模态（诸如LiDAR（光成像、检测和测距）、一组RGB图像和一组热图像）的融合。然而，处理点云并不容易，因为它可能有数百万甚至数亿个点。因此，传统计算机在处理多模态传感器点云数据时经常会崩溃。新兴的量子计算技术可以帮助用户更有效地解决多模态传感器点云处理问题。量子计算硬件的发展正在快速进行，当前的量子计算机存在，每台计算机的量子比特（qubit）数量稳步增加。因此，量子计算有望成为克服高实时计算要求的重要而有效的工具。为了在量子计算机中操作点云，需要解决两个问题，即量子点云表示和量子点云处理。二维图像的量子表示比比皆是。然而，使用量子表示来表达三维图像的方法明显缺乏。此外，为了提供融合多模态传感器数据的基于量子计算的解决方案，表示和处理需要进一步推广到N维量子点云。因此，该项目将涉及N维量子点云的开发和分析，并将对CPC和QPC进行系统的性能比较。这包括定义QPC结果的效率和准确性的度量，例如准备和处理量子点云所需的时间以及QPC结果的统计变化的评估。该项目还将评估N维量子点云如何解决多模态传感器数据中的不确定性问题，从而可以得出比经典信息处理方法更确定的结果。

The large-scale multimodal sensor fusion of internet of things (loT) data can be transformed into a N-dimensional classical point cloud. For example, the transformation may be the fusion of three imaging modalities of different natures such as LiDAR (light imaging, detection, and ranging), a set of RGB images, and a set of thermal images. However, it is not easy to process a point cloud because it can have millions or even hundreds of millions of points. Classical computers therefore often crash when operating a point cloud of multimodal sensor data.The emerging quantum computing technology can help users to solve the multimodal sensor point cloud processing problem more efficiently.The development of the quantum computing hardware is proceeding at a fast pace, and current quantum computers exist, with the number of quantum-bits (qubits) per computer steadily increasing. Quantum computation is therefore expected to become an important and effective tool to overcome the high real-time computational requirements. In order to operate point clouds in quantum computers, there are two problems to be solved, and these are quantum point cloud representation and quantum point cloud processing. Quantum representations of two-dimensional images abound. However, there is a distinct paucity of methods to express a three-dimensional image using quantum representation. Furthermore, to provide a quantum computing based solution for fused multimodal sensor data the representation and processing needs to be further generalized to N-dimensional quantum point clouds.The project will therefore involve the development and analysis of an N-dimensional quantum point cloud, and a systematic metrological comparison between CPC and QPC will be performed. This includes definitions of measures for the efficiency and accuracy of QPC results, such as the time it takes prepare and process the quantum point cloud and the evaluation of the statistical variations of QPC outcomes. The project will also evaluate how an N-dimensional quantum point cloud addresses the problem of uncertainty in multi-modal sensor data, such that precognitive/predictive models can be derived with outcomes of greater certainty than classical information processing methods.