Automated Synthesis of Quantum Circuits using Neural Network

While the ability to build quantum computers is improving dramatically, developing quantum algorithms is very limited and relies on human insight and ingenuity. Although several quantum programming languages have been developed, it is challenging for software developers unfamiliar 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 through a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.