Comparing Quantum Encoding Techniques

TL;DR

This paper compares basis, amplitude, and rotation quantum encoding methods within hybrid quantum-classical neural networks, analyzing their effectiveness, resource requirements, and noise resistance for digit classification tasks.

Contribution

It provides a comparative analysis of fundamental quantum encoding techniques in the context of hybrid quantum-classical machine learning.

Findings

Amplitude encoding achieved higher accuracy.

Rotation encoding showed better noise resistance.

Basis encoding was most resource-efficient.

Abstract

As quantum computers continue to become more capable, the possibilities of their applications increase. For example, quantum techniques are being integrated with classical neural networks to perform machine learning. In order to be used in this way, or for any other widespread use like quantum chemistry simulations or cryptographic applications, classical data must be converted into quantum states through quantum encoding. There are three fundamental encoding methods: basis, amplitude, and rotation, as well as several proposed combinations. This study explores the encoding methods, specifically in the context of hybrid quantum-classical machine learning. Using the QuClassi quantum neural network architecture to perform binary classification of the `3' and `6' digits from the MNIST datasets, this study obtains several metrics such as accuracy, entropy, loss, and resistance to noise, while considering resource usage and computational complexity to compare the three main encoding methods.