Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach
Author:
Affiliation:
1. Department of Computer Science, Appalachian State University, Boone, NC, USA
2. School of Electrical, Computer Engineering, Purdue University, West Lafayette, IN, USA
Funder
Defense Advanced Research Projects Agency Quantum Causality
NeurIPS 2021 Second Workshop on Quantum Tensor Networks in Machine Learning
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Mechanical Engineering,Engineering (miscellaneous),Condensed Matter Physics,Computer Science (miscellaneous),Software
Link
http://xplorestaging.ieee.org/ielx7/8924785/9998549/10269064.pdf?arnumber=10269064
Reference50 articles.
1. Quantum tensor networks, stochastic processes, and weighted automata;adhikary;Proc Int Conf Artif Intell Statist,0
2. Memory compression and thermal efficiency of quantum implementations of nondeterministic hidden Markov models
3. Text-dependent speaker identification using circular hidden Markov models
4. A shape descriptor based on circular hidden Markov model
5. Extreme Dimensionality Reduction with Quantum Modeling
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