Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach

TL;DR

This paper presents Reinforcement Quantum Annealing (RQA), a novel quantum-assisted learning method that interacts with a quantum annealer to improve solutions for complex problems like SAT, showing better results than existing techniques.

Contribution

The paper introduces RQA, a new approach combining reinforcement learning with quantum annealing to enhance solution quality for NP-complete problems.

Findings

RQA outperforms state-of-the-art quantum annealing methods.

RQA finds better solutions with fewer samples.

Effective on benchmark SAT problems using D-Wave 2000Q.

Abstract

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.