Digital-analog quantum convolutional neural networks for image classification

TL;DR

This paper introduces digital-analog quantum kernels integrated with convolutional neural networks to improve medical image classification, achieving better performance with fewer parameters and approaching quantum advantage.

Contribution

It presents a novel hybrid quantum-classical architecture using digital-analog quantum kernels for image classification, demonstrating improved efficiency and accuracy over classical models.

Findings

Outperforms classical models on medical image benchmarks.

Reduces number of parameters needed for accurate classification.

Shows potential for quantum advantage in image recognition.

Abstract

We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, and individual operations as digital steps to implement the protocol. To further improving the detection of complex features, we apply multiple quantum kernels by varying the qubit connectivity according to the hardware constraints. An architecture that combines non-trainable quantum kernels and standard convolutional neural networks is used to classify realistic medical images, from breast cancer and pneumonia diseases, with a significantly reduced number of parameters. Despite this fact, the model exhibits better performance than its classical counterparts and achieves comparable metrics according to public benchmarks. These findings demonstrate the relevance of digital-analog encoding, paving the way for surpassing classical models in image recognition approaching us to quantum-advantage regimes.