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Fidelity-Based Probabilistic Q-Learning for Control of Quantum Systems

CHUNLIN CHEN ; DAOYI DONG ; LI, Han-Xiong ; JIAN CHU ; TARN, Tzyh-Jong

IEEE transaction on neural networks and learning systems, 2014-05, Vol.25 (5), p.920-933

New York, NY: IEEE

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Título:
Fidelity-Based Probabilistic Q-Learning for Control of Quantum Systems
Autor: CHUNLIN CHEN ; DAOYI DONG ; LI, Han-Xiong ; JIAN CHU ; TARN, Tzyh-Jong
Materias: Adaptative systems ; Algorithmics. Computability. Computer arithmetics ; Algorithms ; Applied sciences ; Approximation algorithms ; Artificial intelligence ; Balancing ; Classical and quantum physics: mechanics and fields ; Computer science; control theory; systems ; Control systems ; Control theory. Systems ; Educational institutions ; Exact sciences and technology ; Exploration ; Fidelity ; Heuristic ; Learning ; Learning (artificial intelligence) ; Learning and adaptive systems ; Learning systems ; Physics ; Probabilistic logic ; Probabilistic methods ; probabilistic Q-learning ; Probability distribution ; Probability theory ; Quantum computation ; quantum control ; Quantum information ; Quantum theory ; reinforcement learning ; Theoretical computing
Es parte de: IEEE transaction on neural networks and learning systems, 2014-05, Vol.25 (5), p.920-933
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Descripción: The balance between exploration and exploitation is a key problem for reinforcement learning methods, especially for Q-learning. In this paper, a fidelity-based probabilistic Q-learning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems. In this approach, fidelity is adopted to help direct the learning process and the probability of each action to be selected at a certain state is updated iteratively along with the learning process, which leads to a natural exploration strategy instead of a pointed one with configured parameters. A probabilistic Q-learning (PQL) algorithm is first presented to demonstrate the basic idea of probabilistic action selection. Then the FPQL algorithm is presented for learning control of quantum systems. Two examples (a spin-1/2 system and a Λ-type atomic system) are demonstrated to test the performance of the FPQL algorithm. The results show that FPQL algorithms attain a better balance between exploration and exploitation, and can also avoid local optimal policies and accelerate the learning process.
Editor: New York, NY: IEEE
Idioma: Inglés

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Fidelity-Based Probabilistic Q-Learning for Control of Quantum Systems

CHUNLIN CHEN ; DAOYI DONG ; LI, Han-Xiong ; JIAN CHU ; TARN, Tzyh-Jong

New York, NY: IEEE

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