The Useful Quantum Computing Techniques for Artificial Intelligence Engineers
Jaeho Choi, Seunghyeok Oh, Joongheon Kim
Abstract
The hottest topics for many researchers in the past five years were Artificial Intelligence (AI) and machine learning. With many kinds of researches using machine learning, numerous AI engineers are still emerging. If the center of current research trends is on AI and machine learning, the center of near-future research trends will be on quantum computing techniques. The qubit implementation via superconductivity, diamond NitrogenVacancy (NV) center, ion-trap, and etc. has made quantum computers really exist. And cloud computing has made it possible for researchers around the world to use quantum computers remotely to their researches. The universalization of quantum computing techniques is no longer a story of the distant future, even more so for numerous AI engineers. This paper introduces some useful quantum computing techniques for AI engineers such as Quadratic Unconstrained Binary Optimization (QUBO), Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), and Harrow-Hassidim-Lloyd (HHL) algorithm.