Fast Quantum Convolutional Neural Networks for Low-Complexity Object
  Detection in Autonomous Driving Applications
Hankyul Baek Donghyeon Kim Joongheon Kim
Spurred by consistent advances and innovation in deep learning, object
detection applications have become prevalent, particularly in autonomous
driving that leverages various visual data. As convolutional neural networks
(CNNs) are being optimized, the performances and computation speeds of object
detection in autonomous driving have been significantly improved. However, due
to the exponentially rapid growth in the complexity and scale of data used in
object detection, there are limitations in terms of computation speeds while
conducting object detection solely with classical computing. Motivated by this,
quantum convolution-based object detection (QCOD) is proposed to adopt quantum
computing to perform object detection at high speed. The QCOD utilizes our
proposed fast quantum convolution that uploads input channel information and
re-constructs output channels for achieving reduced computational complexity
and thus improving performances. Lastly, the extensive experiments with KITTI
autonomous driving object detection dataset verify that the proposed fast
quantum convolution and QCOD are successfully operated in real object detection
applications.