Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach
This work addresses the challenge of enhancing quantum annealing efficiency for SAT problems, which is incremental as it builds on existing quantum annealing methods with a novel agent-based approach.
The paper tackles the problem of improving quantum annealing for NP-complete Boolean satisfiability (SAT) problems by introducing a reinforcement quantum annealing (RQA) scheme, where an intelligent agent iteratively finds better Ising Hamiltonians; experimental results on benchmark SAT problems using a D-Wave 2000Q processor show that RQA finds notably better solutions with fewer samples compared to state-of-the-art techniques.
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.