Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata

We introduce the notion of reinforcement quantum annealing (RQA) scheme in which an intelligent agent searches in the space of Hamiltonians and interacts with a quantum annealer that plays the stochastic environment role of learning automata. At each iteration of RQA, after analyzing results (sample...

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Autores principales: Ayanzadeh, Ramin, Halem, Milton, Finin, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224393/
https://www.ncbi.nlm.nih.gov/pubmed/32409743
http://dx.doi.org/10.1038/s41598-020-64078-1
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author Ayanzadeh, Ramin
Halem, Milton
Finin, Tim
author_facet Ayanzadeh, Ramin
Halem, Milton
Finin, Tim
author_sort Ayanzadeh, Ramin
collection PubMed
description We introduce the notion of reinforcement quantum annealing (RQA) scheme in which an intelligent agent searches in the space of Hamiltonians and interacts with a quantum annealer that plays the stochastic environment role of learning automata. At each iteration of RQA, after analyzing results (samples) from the previous iteration, the agent adjusts the penalty of unsatisfied constraints and re-casts the given problem to a new Ising Hamiltonian. As a proof-of-concept, we propose a novel approach for casting the problem of Boolean satisfiability (SAT) to Ising Hamiltonians and show how to apply the RQA for increasing the probability of finding the global optimum. Our experimental results on two different benchmark SAT problems (namely factoring pseudo-prime numbers and random SAT with phase transitions), using a D-Wave 2000Q quantum processor, demonstrated that RQA finds notably better solutions with fewer samples, compared to the best-known techniques in the realm of quantum annealing.
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spelling pubmed-72243932020-05-20 Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata Ayanzadeh, Ramin Halem, Milton Finin, Tim Sci Rep Article We introduce the notion of reinforcement quantum annealing (RQA) scheme in which an intelligent agent searches in the space of Hamiltonians and interacts with a quantum annealer that plays the stochastic environment role of learning automata. At each iteration of RQA, after analyzing results (samples) from the previous iteration, the agent adjusts the penalty of unsatisfied constraints and re-casts the given problem to a new Ising Hamiltonian. As a proof-of-concept, we propose a novel approach for casting the problem of Boolean satisfiability (SAT) to Ising Hamiltonians and show how to apply the RQA for increasing the probability of finding the global optimum. Our experimental results on two different benchmark SAT problems (namely factoring pseudo-prime numbers and random SAT with phase transitions), using a D-Wave 2000Q quantum processor, demonstrated that RQA finds notably better solutions with fewer samples, compared to the best-known techniques in the realm of quantum annealing. Nature Publishing Group UK 2020-05-14 /pmc/articles/PMC7224393/ /pubmed/32409743 http://dx.doi.org/10.1038/s41598-020-64078-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ayanzadeh, Ramin
Halem, Milton
Finin, Tim
Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata
title Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata
title_full Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata
title_fullStr Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata
title_full_unstemmed Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata
title_short Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata
title_sort reinforcement quantum annealing: a hybrid quantum learning automata
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224393/
https://www.ncbi.nlm.nih.gov/pubmed/32409743
http://dx.doi.org/10.1038/s41598-020-64078-1
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