Quantum robot: structure, algorithms and applications

Abstract

A brand-new paradigm of robots–quantum robots–is proposed through the fusion of quantum theory with robot technology. A quantum robot is essentially a complex quantum system which generally consists of three fundamental components: multi-quantum computing units (MQCU), quantum controller/actuator, and information acquisition units. Corresponding to the system structure, several learning control algorithms, including quantum searching algorithms and quantum reinforcement learning algorithms, are presented for quantum robots. The theoretical results show that quantum robots using quantum searching algorithms can reduce the complexity of the search problem from O($N^2)$ in classical robots to O($N\sqrt N)$. Simulation results demonstrate that quantum robots are also superior to classical robots in efficient learning under novel quantum reinforcement learning algorithms. Considering the advantages of quantum robots, some important potential applications are also analyzed and prospected.