Machine learning phase space quantum dynamics approaches

2021 ◽  
Vol 154 (18) ◽  
pp. 184104
Author(s):  
Xinzijian Liu ◽  
Linfeng Zhang ◽  
Jian Liu
Keyword(s):  
Machine Learning ◽  
Phase Space ◽  
Quantum Dynamics ◽  
Learning Phase

Quantum Dynamics in Phase Space

1994 ◽  
pp. 475-484
Author(s):  
A. Smerzi ◽  
V. Kondratyev ◽  
A. Bonasera
Keyword(s):  
Phase Space ◽  
Quantum Dynamics

scholarly journals Cognitive Driven Multilayer Self-Paced Learning with Misclassified Samples

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Qi Zhu ◽  
Ning Yuan ◽  
Donghai Guan
Keyword(s):  
Machine Learning ◽  
Learning Process ◽  
Human Learning ◽  
Learning Phase ◽  
Machine Learning Algorithms ◽  
Learning Difficulty ◽  
Learning Models ◽  
Negative Effects ◽  
Initial Learning

In recent years, self-paced learning (SPL) has attracted much attention due to its improvement to nonconvex optimization based machine learning algorithms. As a methodology introduced from human learning, SPL dynamically evaluates the learning difficulty of each sample and provides the weighted learning model against the negative effects from hard-learning samples. In this study, we proposed a cognitive driven SPL method, i.e., retrospective robust self-paced learning (R2SPL), which is inspired by the following two issues in human learning process: the misclassified samples are more impressive in upcoming learning, and the model of the follow-up learning process based on large number of samples can be used to reduce the risk of poor generalization in initial learning phase. We simultaneously estimated the degrees of learning-difficulty and misclassified in each step of SPL and proposed a framework to construct multilevel SPL for improving the robustness of the initial learning phase of SPL. The proposed method can be viewed as a multilayer model and the output of the previous layer can guide constructing robust initialization model of the next layer. The experimental results show that the R2SPL outperforms the conventional self-paced learning models in classification task.

Quantum Dynamics in Phase Space

1968 ◽  
Vol 21 (3) ◽  
pp. 180-183 ◽  
Author(s):  
G. S. Agarwal ◽  
E. Wolf
Keyword(s):  
Phase Space ◽  
Quantum Dynamics

Adaptive Modeling of Physical Systems Based on Affine Transform and its Application for Machine Learning

2008 ◽  
Vol 20 (5) ◽  
pp. 750-756
Author(s):  
Shingo Nakamura ◽  
◽  
Shuji Hashimoto
Keyword(s):  
Machine Learning ◽  
Phase Space ◽  
Physical System ◽  
System Structure ◽  
Input Output ◽  
Adaptive Modeling ◽  
Physical Systems ◽  
Affine Transform ◽  
Physical Information

We describe the adaptive modeling of a physical system using the affine transform and its application to machine learning. We previously proposed a method to implement machine learning in physical hardware, where we built a simulator based on actual hardware input/output, and used it to optimize a controller. The method decreases stress on hardware because the controller is optimized by software via the simulator. Moreover, it does not require specific physical information on hardware. We also did not need to formulate hardware kinematics. When hardware changes, however, optimization must be redone to build the simulator -a clearly inefficient procedure. We therefore considered using previous optimization results when reoptimizing for new hardware. In the physical system, the aspect of the phase space does not vary much if the system structure remains the same. We applied affine transform to phase space of the physical system, to remodel the simulator for new hardware characteristics triggered by parameter changes. We used the remodeled simulator in machine learning to reoptimize the controller. In experiments, we used the swing-up pendulum problem to evaluate our proposal, comparing our proposal and original methods and finding that our proposal accelerates reoptimization.

Phase space path integrals in Monte Carlo quantum dynamics

1996 ◽  
Vol 104 (16) ◽  
pp. 6265-6277 ◽  
Author(s):  
Stavros Caratzoulas ◽  
Philip Pechukas
Keyword(s):  
Monte Carlo ◽  
Phase Space ◽  
Quantum Dynamics ◽  
Path Integrals
Sign in / Sign up

Export Citation Format

Share Document