2,776 research outputs found
Online quantum mixture regression for trajectory learning by demonstration
In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online Expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community
Bayesian Inverse Quantum Theory
A Bayesian approach is developed to determine quantum mechanical potentials
from empirical data. Bayesian methods, combining empirical measurements and "a
priori" information, provide flexible tools for such empirical learning
problems. The paper presents the basic theory, concentrating in particular on
measurements of particle coordinates in quantum mechanical systems at finite
temperature. The computational feasibility of the approach is demonstrated by
numerical case studies. Finally, it is shown how the approach can be
generalized to such many-body and few-body systems for which a mean field
description is appropriate. This is done by means of a Bayesian inverse
Hartree-Fock approximation.Comment: LaTex, 32 pages, 19 figure
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