Conglomeration of deep neural network and quantum learning for object detection: Status quo review

The practice of deep neural framework specific to convolutional neural networks (ConNeuNets) in domain of object detection is substantial. The existing deep ConNeuNets give higher accuracy provided higher value of training time on a high-end graphical processing unit (GPU). On the other hand, in classification domain of object detection, quantum learning algorithms have shown temporal exponential reduction. However, this has happened in regard to relatively smaller sized dataset when compared to usual data-set-size employment on state-of-the-art deep ConNeuNets. Considering the training-time for conventional deep network-model, power consumption while training the deep model on a dedicated hardware and present state of quantum computing hardware it is reliable to examine the prospect of interaction between quantum algorithms and deep model paradigms. Approximately 69% of output index in case of quantum-gates-based fabricated system of quantum volume (4096) and rise of explainable domain of study in deep learning due to its non-exact comprehension invoke to probe into the same prospect of interaction. So, this paper tries to review the existing methods and prospect of object detection using quantum learning concepts on existing deep neural framework.