A Review on Quantum Image Processing
This paper is a review of research in quantum image processing (QIP), storage, and retrieval. It discusses current issues with silicon based computing on processing big data for machine learning tasks such as image recognition and how quantum computation can address these challenges. First this paper will introduce the challenges Moore’s law presents to traditional computer processors in addition to an introduction to quantum computers and how they address these. The paper will then introduce how quantum computation evolved into the field of image processing followed by a discussion on the advantages and disadvantages found in current research and applications.
I. Introduction
Moore’s law is a common discussion topic involving the evolution of transistors in computer processors. It is the observation that the number of transistors in a dense integrated circuit doubles approximately every two years. This prediction is based on observation and projection of transistor size rather than any natural phenomena. However, this prediction has proved accurate which leads to worry because at some point, transistors can only shrink so small and still prove effective. With the demand for storage, retrieval, and processing of big data sets in machine learning, the need for the transistors to shrink and improve efficiency is high, but the ability to do so is limited.
