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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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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. |
format | Online Article Text |
id | pubmed-7224393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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