Quantum computing has shown great potential and advantages in solving integer factorization and disordered database search. However, it is not easy to solve specific problems with quantum computing device efficiently and widely, because a lot of professional background knowledge is required. In order to solve this problem, we propose an optimization problem’s automatic hybird quantum framework (OpAQ) for solving user-specified problems on a hybrid computing architecture including both quantum and classical computing resources. Such a solver can allow nonprofessionals who are not familiar with quantum physics and quantum computing to use quantum computing device to solve some classically difficult problems easily. Combinatorial optimization problem is one of the most important problems in both academic and industry. In this paper, we mainly focus on these problems and solve them with OpAQ, which is based on quantum approximation optimization algorithm (QAOA). We evaluate the performance of our approach in solving Graph Coloring, Max-cut, Traveling Salesman and Knapsack Problem. The experimental results show that quantum solver can achieve almost the same optimal solutions with the classical.

中文翻译：

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混合量子架构上组合问题的自动方法

量子计算在解决整数分解和无序数据库搜索方面显示出巨大的潜力和优势。然而，量子计算设备要高效、广泛地解决具体问题并不容易，因为需要大量的专业背景知识。为了解决这个问题，我们提出了一种优化问题的自动混合量子框架（OpAQ），用于在包括量子和经典计算资源的混合计算架构上解决用户指定的问题。这样的求解器可以让不熟悉量子物理和量子计算的非专业人士使用量子计算设备轻松解决一些经典难题。组合优化问题是学术界和工业界最重要的问题之一。在本文中，我们主要关注这些问题并使用基于量子近似优化算法（QAOA）的OpAQ来解决它们。我们评估了我们的方法在解决图着色、最大切割、旅行推销员和背包问题方面的性能。实验结果表明，量子求解器可以达到与经典求解几乎相同的最优解。