  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 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 state-of-the-art techniques in the realm of
quantum annealing.

Quantum Physics;Artificial Intelligence;Machine Learning