Predict better with less training data using a QNN

TL;DR

This paper introduces a hybrid quantum-classical neural network that leverages quantum encoding to improve image analysis in industrial quality control, achieving better predictions with less training data.

Contribution

It presents a novel QNN algorithm that integrates quantum convolutions into classical CNNs, demonstrating quantum advantage in real-world image classification tasks.

Findings

Achieves better predictions with fewer training samples

Demonstrates quantum advantage in industrial image analysis

Uses quantum encoding for improved data representation

Abstract

Over the past decade, machine learning revolutionized vision-based quality assessment for which convolutional neural networks (CNNs) have now become the standard. In this paper, we consider a potential next step in this development and describe a quanvolutional neural network (QNN) algorithm that efficiently maps classical image data to quantum states and allows for reliable image analysis. We practically demonstrate how to leverage quantum devices in computer vision and how to introduce quantum convolutions into classical CNNs. Dealing with a real world use case in industrial quality control, we implement our hybrid QNN model within the PennyLane framework and empirically observe it to achieve better predictions using much fewer training data than classical CNNs. In other words, we empirically observe a genuine quantum advantage for an industrial application where the advantage is due to superior data encoding.