Optimal Quantum Circuit Design via Unitary Neural Networks
The process of translating a quantum algorithm into a form suitable for implementation on a quantum computing platform is crucial but yet challenging. This entails specifying quantum operations with precision, a typically intricate task. In this paper, we present an alternative approach: an automated method for synthesizing the functionality of a quantum algorithm into a quantum circuit model representation. Our methodology involves training a neural network model using diverse input-output mappings of the quantum algorithm. We demonstrate that this trained model can effectively generate a quantum circuit model equivalent to the original algorithm. Remarkably, our observations indicate that the trained model achieves near-perfect mapping of unseen inputs to their respective outputs.