Exploring Quantum Neural Networks for the Discovery and Implementation of Quantum Error-Correcting Codes

By A. Chalkiadakis et al

Published on April 13, 2023

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Table of Contents

Abstract
Introduction
Quantum Neural Network's Architecture
Enhancing QNN Performance with Conjugate Layers
Quantum Autoencoders for Bit Flip Error-Correction
Limitations of this Approach

Summary

The document explores the use of Quantum Neural Networks for discovering and implementing quantum error-correcting codes. It showcases the efficacy of Quantum Neural Networks in correcting errors in logical qubit states. The paper discusses the architecture of Quantum Neural Networks, the training process, and the performance evaluation through a cost function. It introduces the concept of conjugate layers to enhance training performance. Additionally, it delves into Quantum Autoencoders for Bit Flip Error-Correction, detailing the training process and model evaluation. The limitations of this approach, including the impact of multiple errors on error correction accuracy, are also discussed.