Extreme spin squeezing from deep reinforcement learning
نویسندگان
چکیده
منابع مشابه
Entanglement and extreme spin squeezing.
For any mean value of a Cartesian component of a spin vector we identify the smallest possible uncertainty in any of the orthogonal components. The corresponding states are optimal for spectroscopy and atomic clocks. We show that the results for different spin J can be used to identify entanglement and to quantify the depth of entanglement in systems with many particles. With the procedure deve...
متن کاملOperation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm
: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...
متن کاملDeep Reinforcement Learning from Human Preferences
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including At...
متن کاملCollaborative Deep Reinforcement Learning
Besides independent learning, human learning process is highly improved by summarizing what has been learned, communicating it with peers, and subsequently fusing knowledge from dierent sources to assist the current learning goal. is collaborative learning procedure ensures that the knowledge is shared, continuously rened, and concluded from dierent perspectives to construct a more profound...
متن کاملDeep Reinforcement Learning
Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review A
سال: 2019
ISSN: 2469-9926,2469-9934
DOI: 10.1103/physreva.100.041801