Application of quantum computing in physics

Abstract

Quantum computing is based on quantum mechanics and its phenomena. It promises to provide high computational power, high speed compare to classical computers and solve unsolvable problems of classical computers. Starting with the basics of quantum computing and its advantages, application of quantum computing in physics is the focus of this thesis. As application of computers, Machine learning systems are increasingly reaching the boundaries of classical computational models as the amount of data continues to grow. In this way, quantum computing power can help with machine learning problems. Quantum machine learning is the study of how to design and deploy quantum software to enable machine learning that is faster than that of classical computers. Many body problem can be better under stood by simulating on computers. Classical computers are not that efficient this task. Quantum simulators would allow researchers to investigate new physical phenomena by tackling this issues. This thesis describes our work of using quan tum computing for experimental high energy physics data analysis and simulation of atomic nuclei.