Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata View Full Text


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Article Info

DATE

2020-05-14

AUTHORS

Ramin Ayanzadeh, Milton Halem, Tim Finin

ABSTRACT

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. More... »

PAGES

7952

References to SciGraph publications

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  • 2019-01-21. Factoring larger integers with fewer qubits via quantum annealing with optimized parameters in SCIENCE CHINA PHYSICS, MECHANICS & ASTRONOMY
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  • 2012-08-13. Finding low-energy conformations of lattice protein models by quantum annealing in SCIENTIFIC REPORTS
  • 2008. Z3: An Efficient SMT Solver in TOOLS AND ALGORITHMS FOR THE CONSTRUCTION AND ANALYSIS OF SYSTEMS
  • 2017-02-21. Prime factorization using quantum annealing and computational algebraic geometry in SCIENTIFIC REPORTS
  • 2019-02-19. Embedding Inequality Constraints for Quantum Annealing Optimization in QUANTUM TECHNOLOGY AND OPTIMIZATION PROBLEMS
  • 2017-09-14. Quantum machine learning in NATURE
  • 2007-09. Quantum to classical and back in NATURE PHYSICS
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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-020-64078-1

    DOI

    http://dx.doi.org/10.1038/s41598-020-64078-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1127593741

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/32409743


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