论文标题
AutoQC：使用神经网络自动合成量子电路
AutoQC: Automated Synthesis of Quantum Circuits Using Neural Network
论文作者
论文摘要
虽然构建量子计算机的能力正在显着提高，但开发量子算法是有限的，依赖于人类的洞察力和创造力。尽管已经开发了许多量子编程语言，但对于不熟悉量子计算来学习和使用这些语言的软件开发人员来说，这是具有挑战性的。因此，有必要开发工具以自动支持开发新的量子算法和程序。本文提出了AutoQC，这是一种使用输入和输出对的神经网络自动合成量子电路的方法。我们考虑一个量子电路是量子门的序列，并通过在每个步骤中与神经网络优先级来合成量子电路。实验结果突出了自动QC以较低的成本合成一些必需量子电路的能力。
While the ability to build quantum computers is improving dramatically, developing quantum algorithms is limited and relies on human insight and ingenuity. Although a number of quantum programming languages have been developed, it is challenging for software developers who are not familiar with quantum computing to learn and use these languages. It is, therefore, necessary to develop tools to support developing new quantum algorithms and programs automatically. This paper proposes AutoQC, an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs. We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing with a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.
