Optimal Quantum Circuit Design via Unitary Neural Networks

TL;DR

This paper introduces a neural network-based method for automatically synthesizing quantum circuits from algorithms, enabling efficient and accurate translation of quantum algorithms into implementable circuit models.

Contribution

It presents a novel neural network approach to quantum circuit synthesis, improving automation and accuracy over traditional manual methods.

Findings

The trained neural network accurately maps unseen inputs to outputs.

The method effectively generates quantum circuits equivalent to original algorithms.

Near-perfect input-output mapping achieved by the trained model.

Abstract

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.