Corpus ID: 209531782

Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach

@article{Ayanzadeh2020ReinforcementQA,
  title={Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach},
  author={Ramin Ayanzadeh and M. Halem and Tim Finin},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.00234}
}
We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest. As a proof-of-concept, we propose a novel approach for reducing the NP-complete problem of Boolean satisfiability (SAT) to minimizing Ising Hamiltonians and show how to apply the RQA for increasing the probability of… Expand
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