Year: 2019
Type: preprint
Abstract: We implement the reinforcement learning agent for a spin-1 atomic system to prepare spin squeezed state from given initial state. Proximal policy gradient (PPO) algorithm is used to deal with continuous external control field and final optimized protocol is given by a stochastic policy. In both mean-field system and two-body quantum system, RL agent finds the optimal policies. In many-body quantum system, it also gives polices that outperform purely greedy policy and optimized adiabatic passage. These polices given by RL agent have good physical interpretability in phase space and may help us to understand quantum dynamics. In fact, RL could be highly versatile in quantum optimal control problems.
Source: arXiv (Cornell University)
Authors: Chen, Jun-Jie, Xue, Ming
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2914709595