QUANTUM COMPUTING FOR DESIGNING BEHAVIORAL MODEL AND QUANTUM MACHINE LEARNING ON A HUMANOID ROBOT

Abstract

Today’s robots are controlled with classical computers that make decisions based on algorithms designed for a classical computing architecture. However, the amount of data increases continuously - a fact which pushes classical computers that control these robots to their limits. In comparison to classical computing, fundamentally different concepts of physics are the basis of quantum computing and thereby quantum computing has the potential to solve specific computational problems more efficiently. In addition to the aforementioned complications, the subgroup of robots that are subject to the research of this paper, namely humanoid robots, not only need more computational capacity but also require an algorithmic design based on a behavioral model which avoids causing antipathy when interacting with human beings. Using a real quantum computer with quantum phenomena such as superposition, entanglement, and quantum parallelism, new behavioral models and algorithms can be designed to improve human robot interaction and speed up machine learning processes. This paper promotes a hybrid-controlled robot using the combination of a classical computer- and quantum computer. With four quantum circuits to define a behavioral model and three different quantum machine learning algorithms we show the advantage of the hybrid-controlled robot.