AutoQC: Automated Synthesis of Quantum Circuits Using Neural Network
This addresses the problem of limited quantum algorithm development for non-expert software developers, though it appears incremental as it builds on existing neural network methods for synthesis.
The paper tackles the challenge of automating quantum circuit synthesis for developers unfamiliar with quantum computing by proposing AutoQC, which uses a neural network to generate circuits from input-output pairs, achieving lower-cost synthesis of essential quantum circuits.
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.