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On the Universality of Memcomputing Machines.

Authors :
Pei, Yan Ru
Traversa, Fabio L.
Di Ventra, Massimiliano
Source :
IEEE Transactions on Neural Networks & Learning Systems. Jun2019, Vol. 30 Issue 6, p1610-1620. 11p.
Publication Year :
2019

Abstract

Universal memcomputing machines (UMMs) represent a novel computational model in which memory (time nonlocality) accomplishes both tasks of storing and processing of information. UMMs have been shown to be Turing-complete, namely, they can simulate any Turing machine. In this paper, we first introduce a novel set theory approach to compare different computational models and use it to recover the previous results on Turing-completeness of UMMs. We then relate UMMs directly to liquid-state machines (or “reservoir-computing”) and quantum machines (“quantum computing”). We show that UMMs can simulate both types of machines, hence they are both “liquid-” or “reservoir-complete” and “quantum-complete.” Of course, these statements pertain only to the type of problems these machines can solve and not to the amount of resources required for such simulations. Nonetheless, the set-theoretic method presented here provides a general framework which describes the relationship between any computational models. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2162237X
Volume :
30
Issue :
6
Database :
Academic Search Index
Journal :
IEEE Transactions on Neural Networks & Learning Systems
Publication Type :
Periodical
Accession number :
136696641
Full Text :
https://doi.org/10.1109/TNNLS.2018.2872676