Entangled state preparation enhanced by reinforcement learning

Feng Chen, Shuai-Feng Guo

Gerhard Stenzel, Michael Kölle, Tobias Rohe +3 more

Tingting Li, Yiming Zhao, Yong Wang +3 more

Tingting Li, Yiming Zhao, Yong Wang +3 more

Jincheng Zhang

Quantum entanglement offers a fundamentally different approach to learning, potentially surpassing the limitations of classical reinforcement learning. This paper introduces a novel quantum-enhanced reinforcement learning algorithm leveraging entangled states for instantaneous information transfer. We propose a mechanism that utilizes a "hidden feedback" system, enabling gradients to propagate via entangled states, accelerating learning and promoting adaptive behavioral patterns. The core claim is that this approach enhances the learning efficiency and generalization capabilities of reinforcement learning compared to traditional methods. We explore the potential of this approach and present preliminary results demonstrating its effectiveness.

Xiaolong Zhao, Yiming Zhao, Ming Li +4 more

Abstract A scheme leveraging reinforcement learning to engineer control fields for generating non‐classical states is proposed. It is exemplified by the application to prepare spin‐squeezed states for an open collective spin model where a linear control field is designed to govern the dynamics. The reinforcement learning agent determines the temporal sequence of control pulses, commencing from a coherent spin state in an environment characterized by dissipation and dephasing. Compared to the constant control scenario, this approach provides various control sequences maintaining collective spin squeezing and entanglement. It is observed that denser application of the control pulses enhances the performance of the outcomes. However, there is a minor enhancement in the performance by adding control actions. The proposed strategy demonstrates increased effectiveness for larger systems. Thermal excitations of the reservoir are detrimental to the control outcomes. Feasible experiments are suggested to implement this control proposal based on the comparison with the others. The extensions to continuous control problems and another quantum system are discussed. The replaceability of the reinforcement learning module is also emphasized. This research paves the way for its application in manipulating other quantum systems.