QUANTUM COMPUTING: ITS APPLICATIONS IN MACHINE LEARNING AND OTHER AREAS

Classical computers have been present for a long time and they have played a significant role in scientific advancements. Quantum computing has shown good results in solving complex problems. Quantum computers use phenomena of quantum superposition and quantum entanglement to form states that scale exponentially with the number of qubits or quantum bits [1]. Classical computers use individual bits, 0 and 1 to store information as binary data & quantum computers use the probability of state before it is measured [2]. Therefore, it gives them a potential to process exponentially more data as compared to classical computers. Unlike classical computers that use binary bits, quantum computers use qubits that are produced by quantum state of object to perform operations. Since, these qubits are quantum, they follow phenomena like quantum superposition and entanglement. Superposition is ability of a quantum system to be in multiple states at the same time. Entanglement is the strong correlation among quantum particles. These phenomena help quantum computers work with 0, 1 and superposition of 0 and 1, giving it advantage of doing complex calculations that classical systems cannot do or take a significant amount of time to get desired results [3]. Quantum computing is used because of its potential for changing time and space complexity of many algorithms we have been using as a solution to linear system of equations [4]. Quantum simulation is one of the most prominent areas of quantum computers, it has the potential to solve the complexities of molecular and chemical interactions which can lead to the discovery of new medicines and materials. Various applications of quantum computing in several significant areas of computer science, such as cryptography, machine learning, deep learning and quantum simulations. They also use various real-life scenarios such as risk analysis, logistics and satellite communication [6].