Abstract
Quantum computing has aided in the advancement of Artificial Intelligence and Machine Learning technology. In recent years, Quantum Computing has grown in popularity, and Artificial Intelligence has emerged as one of its key application areas. The use of quantum computing, a novel technology that processes data using quantum physics theories, may dramatically boost the speed and effectiveness of machine learning. Quantum computing is based on quantum physics, which varies from classical physics in numerous ways. In quantum physics, particles such as electrons and photons, which can exist in several states at the same time, can be used to represent information. This means that quantum computers can process massive amounts of data far faster than ordinary computers, as well as solve complex problems that normal computers find difficult. Quantum computing can be used to speed up the process of doing complex computations and simulations in the field of Machine Learning. A quantum computer, for example, may be used to rapidly evaluate large data sets and identify patterns that would be difficult to detect using traditional computing approaches. Obviously, the use of Quantum Computing can improve the efficiency of Machine Learning systems. Even Artificial Intelligence systems can be made significantly more effective and efficient by using a quantum computer to examine data and decide the most effective ways to carry out various tasks. This type of optimization has the potential to dramatically increase the efficiency of Artificial Intelligent systems while also cutting maintenance costs. In this chapter, the basic introduction about Quantum computing, Quantum Physics, types of Quantum computers, and their characteristics are depicted. Later, the advantages and disadvantages of quantum computers are derived, and further, some of the real-time applications of quantum computers in the most prevailing domains such as drug discovery, financial modeling, weather forecasting, traffic management, and environmental modeling are explained. Finally, the challenges in implementing the quantum computers are elucidated.
