  Underwater images taken from autonomous underwater vehicles (AUV's) often
suffer from low light, high turbidity, poor contrast, motion-blur and excessive
light scattering and hence require image enhancement techniques for object
recognition. Machine learning methods are being increasingly used for object
recognition under such adverse conditions. These enhanced object recognition
methods of images taken from AUV's has potential applications in underwater
pipeline and optical fibre surveillance, ocean bed resource extraction, ocean
floor mapping, underwater species exploration, etc. While the classical machine
learning methods are very efficient in terms of accuracy, they require large
datasets and high computational time for image classification. In the current
work, we use quantum-classical hybrid machine learning methods for real-time
under-water object recognition on-board an AUV for the first time. We use
real-time motion-blurred and low-light images taken from an on-board camera of
AUV built in-house and apply existing hybrid machine learning methods for
object recognition. Our hybrid methods consist of quantum encoding and
flattening of classical images using quantum circuits and sending them to
classical neural networks for image classification. The results of hybrid
methods carried out using Pennylane based quantum simulators both on GPU and
using pre-trained models on an on-board NVIDIA GPU chipset are compared with
results from corresponding classical machine learning methods. We observe that
the hybrid quantum machine learning methods show an efficiency greater than
65\% and reduction in run-time by one-thirds and require 50\% smaller dataset
sizes for training the models compared to classical machine learning methods.
We hope that our work opens up further possibilities in quantum enhanced
real-time computer vision in autonomous vehicles.
