scholarly journals Restricted Boltzmann machine learning for solving strongly correlated quantum systems

2017 ◽  
Vol 96 (20) ◽  
Author(s):  
Yusuke Nomura ◽  
Andrew S. Darmawan ◽  
Youhei Yamaji ◽  
Masatoshi Imada
Keyword(s):  
Machine Learning ◽  
Quantum Systems ◽  
Restricted Boltzmann Machine ◽  
Strongly Correlated ◽  
Boltzmann Machine ◽  
Correlated Quantum Systems

scholarly journals Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ritaban Dutta ◽  
Cherry Chen ◽  
David Renshaw ◽  
Daniel Liang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Shape Memory ◽  
Video Data ◽  
The Body ◽  
Restricted Boltzmann Machine ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Boltzmann Machine ◽  
High Level

AbstractExtraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.

FAST MAXIMUM ENTROPY ALGORITHM FOR ILL-POSED PROBLEMS

1994 ◽  
Vol 05 (06) ◽  
pp. 987-995 ◽  
Author(s):  
S.V. MESHKOV ◽  
D.V. BERKOV
Keyword(s):  
Maximum Entropy ◽  
Inequality Constraints ◽  
Quantum Systems ◽  
Quadratic Functional ◽  
Strongly Correlated ◽  
Imaginary Time ◽  
Linear Inequality Constraints ◽  
Ill Posed ◽  
Correlated Quantum Systems ◽  
Maxent Approach

The fast algorithm of the Maximum Entropy (MaxEnt) numerical solution of the linear inverse problem is described. The minimization of a general functional intrinsic to the MaxEnt approach is reduced to an iteration procedure with each step being a constrained least-squares problem (minimization of a quadratic functional with linear inequality constraints). The algorithm is structurally simple and can be assembled from blocks available in standard program libraries. The algorithm is tested on “toy” tasks with exponential kernel, as well as on practical problems of the recovery of the spectral density of strongly correlated quantum systems from the imaginary time Green’s functions obtained by Monte Carlo.

scholarly journals Quantum circuits for strongly correlated quantum systems

2009 ◽  
Vol 79 (3) ◽  
Author(s):  
Frank Verstraete ◽  
J. Ignacio Cirac ◽  
José I. Latorre
Keyword(s):  
Quantum Systems ◽  
Quantum Circuits ◽  
Strongly Correlated ◽  
Correlated Quantum Systems
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