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

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.