The efficient resolution of optimization problems is one of the key issues in today's industry. This task relies mainly on classical algorithms that present scalability problems and processing limitations. Quantum computing has emerged to challenge these types of problems. In this paper, we focus on the Metropolis-Hastings quantum algorithm that is based on quantum walks. We use this algorithm to build a quantum software tool called Quantum Metropolis Solver (QMS). We validate QMS with the N-Queen problem to show a potential quantum advantage in an example that can be easily extrapolated to an Artificial Intelligence domain. We carry out different simulations to validate the performance of QMS and its configuration.
翻译：高效解决优化问题是当今行业的关键问题之一。 这项任务主要依赖典型的算法,这些算法提出了可缩放问题和处理限制。 量子计算已经出现,以挑战这类问题。 在本文中,我们侧重于以量子行走为基础的大都市- 利用量子算法。 我们用这种算法来建立一个量子软件工具,称为量子大都会溶解器(QMS QMS ) 。 我们用N Queen 问题验证了QMS QMS 和 N Queen 问题, 以便在一个可以很容易被推断为人工智能域的例子中显示潜在的量子优势。 我们用不同的模拟来验证QMS 及其配置的性能 。
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