Quantum Machine Learning Algorithm for Optimizing Image Data Using Enhanced Quantum Classification Techniques

Abstract

With the massive collection of data and the usage of various predictive modeling with the techniques for images, the need for machine learning came into existence. Dealing with extremely large scales of data, and the need to solve complex problems which trace back to the origin, the need for implementation of quantum techniques became essential. Quantum theory accesses the power of computing at atomic levels and as a current trend, combines with machine learning which helps in speeding up the process and getting very high levels of accuracy. Quantum image processing helps in analyzing the images better, reducing noise and distortion factors, this gives a multi-point view of the image and generates better classification results. Image processing usually deals with all the features on an image, and utilizes image matrices to extract and compare information, this usually results in a loss/lossless, when images are compressed, transformed, and when noise reduction techniques are applied to the images. A typical image lattice gives far fewer details about the image, while analyzing it with a quantum system, helps achieve high accuracy for classification and regression tasks. The Representation of Quantum Images done in our project to classify images using an Enhanced Quantum Representation help in handling details better and obtaining a multidimensional view of the data. The goal is to analyze digital images and make a less intensive model that requires minimal training and gives out extremely good results for classification, segmentation, and other highly intensive tasks for large images like annotation.