Smart Parking Management System Using AI Enhanced by Quantum Network

Abstract

Managing parking space in city areas is a big issue that impacts traffic, air cleanliness, and how easy it is for people to find a place to park. In this research, the authors are proposing a new approach to overcome this problem using quantum computing techniques, specifically quantum image processing and quantum machine learning, to detect vacant parking spaces from live video feeds. This new method uses the strong points of quantum algorithms for spotting patterns and objects. The methodology involves pre-processing parking lot video data and employing quantum image processing techniques like quantum wavelet transforms and quantum pattern recognition algorithms to identify vacant spaces. Moreover, the authors look into quantum machine learning models, such as quantum neural networks and quantum support vector machines, to make the detection of open spots more accurate. They test how well these quantum methods work by using a big mix of parking lot cases and comparing them with traditional machine learning methods like convolutional neural networks. The findings show that bringing quantum computing into parking space management systems can work well and has possible upsides, shining a light on where quantum algorithms do better and pointing out what needs more work in future studies. In this project, the authors dived into how quantum computing can be used in seeing things through computers and managing city infrastructure.