Learn More
In [Dong and Petersen, Sliding Mode Control of Two-Level Quantum Systems, arXiv:1009.0558, quant-ph, 2010], a sliding mode control approach has been proposed for twolevel quantum systems to deal with bounded uncertainties in the system Hamiltonian. This paper further extends these results in two directions. One extension is to consider the effect of(More)
The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum(More)
Autonomous mobile robots have been widely studied and applied not only as a test bed to academically demonstrate the achievement of artificial intelligence but also as an essential component of industrial and home automation. Mobile robots have many potential applications in routine or dangerous tasks such as delivery of supplies in hospitals, cleaning of(More)
A kind of brand-new robot, quantum robot, is proposed through fusing quantum theory with robot technology. Quantum robot is essentially a complex quantum system and it is generally composed of three fundamental parts: MQCU (multi quantum computing units), quantum controller/actuator, and information acquisition units. Corresponding to the system structure,(More)
Robust control design for quantum systems has been recognized as a key task in the development of practical quantum technology. In this paper, we present a systematic numerical methodology of sampling-based learning control (SLC) for control design of quantum systems with uncertainties. The SLC method includes two steps of “training” and “testing”. In the(More)
Controlling the multi-qubit system is a key task for practical quantum information processing. In this paper, the control problem of five-qubit is studied. A novel quantum reinforcement learning algorithm based on quantum superposition principle is proposed for the quantum control problem. The simulated result shows that quantum reinforcement learning can(More)
In this paper, a quantum reinforcement learning method is proposed for repeated game theory. First, the quantum reinforcement learning algorithm is introduced based on quantum state superposition principle and its superiority is analyzed. Then, it is applied to repeated games and the experiments show its effectiveness. Related issues are also discussed(More)