Using Quantum Circuits with Convolutional Neural Networks for Multi-Object Detection and Classification

Multi-Object classification, detection, and recognition are the most often areas where it is already making an impact using the classical machine learning paradigm. Since the first quantum computers appeared and have allowed an exponential increase in the speed of solving NP-complete problems. There has been a rapid increase in interest in quantum algorithms for object recognition and detection. The goal of this paper is to offer a solution to the problem of research and development of quantum algorithms and methods on various real-time applications. The implementation of quantum-classical algorithms enables the conversion of a classical image into the quantum state, to predict the object localization through bounding box and multi-label classification. Quantum machine learning (QML) can process image data faster and more accurately than classical computers; potentially save costs in broader technology development, and support the current era of intermediate-scale quantum technology.

In this paper, we propose a classical and quantum algorithm-based model with quantum circuits using publicly repository datasets such as object autonomous vehicles and traffic sign images for recognition, detection, and classification. Our results demonstrate notable performance with improved accuracy values after combining a quantum circuit with a convolutional neural network and can efficiently classify the multi-object in the image with a location bounding box. With image classification and object detection, we expect to see quantum machine learning become an even more integral part of developing novel transportation solutions and can discover innovative research areas where quantum computing can be a game-changer.