论文标题
增强量子退火：一种量子辅助学习自动机方法
Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach
论文作者
论文摘要
我们介绍了增强量子退火（RQA）方案，其中智能代理与Quantum Enealeler相互作用，该量子退火器扮演学习自动机的随机环境角色，并试图在给定的利益问题上找到更好的Ising Hamiltonians。作为概念验证，我们提出了一种新颖的方法，用于减少布尔可满足性的NP完整问题（SAT），以最大程度地减少伊斯丁汉密尔顿人，并展示如何应用RQA来增加找到全局最佳最佳的可能性。我们对两个不同基准SAT问题的实验结果（即使用D-WAVE 2000Q量子处理器考虑了伪p-prime数字，并随机转换与随机SAT），这表明，与量子死亡领域中的最新技术相比，RQA发现了具有较少样品的较少样品的更好的解决方案。
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.
