Optimal Quantum Circuit Design via Unitary Neural Networks
This addresses a crucial but intricate problem in quantum computing for researchers and engineers, though it appears incremental as it builds on existing neural network techniques.
The paper tackles the challenge of translating quantum algorithms into implementable quantum circuits by introducing an automated method using neural networks, achieving near-perfect mapping of unseen inputs to outputs.
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.