Towards Continuous-variable Quantum Neural Networks for Biomedical Imaging

TL;DR

This paper explores the application of continuous-variable quantum neural networks to biomedical image classification, demonstrating their potential and resilience compared to classical and discrete-variable quantum models.

Contribution

It presents a feasibility study of CV-QCNNs for medical imaging, constructing photonic circuits with Gaussian gates and evaluating their performance on MedMNIST datasets.

Findings

CV-QCNNs achieve competitive classification accuracy.

CV models show resilience to Gaussian noise.

Trade-offs between CV and DV quantum paradigms are identified.

Abstract

Continuous-variable (CV) quantum computing offers a promising framework for scalable quantum machine learning, leveraging optical systems with infinite-dimensional Hilbert spaces. While discrete-variable (DV) quantum neural networks have shown remarkable progress in various computer vision tasks, CV quantum models remain comparatively underexplored. In this work, we present a feasibility study of continuous-variable quantum neural networks (CV-QCNNs) applied to biomedical image classification. Utilizing photonic circuit simulation frameworks, we construct CV quantum circuits composed of Gaussian gates, such as displacement, squeezing, rotation, and beamsplitters to emulate convolutional behavior. Our experiments are conducted on the MedMNIST dataset collection, a set of annotated medical image benchmarks for multiple diagnostic tasks. We evaluate CV-QCNN's performance in terms of classification accuracy, model expressiveness, and resilience to Gaussian noise, comparing against classical CNNs and equivalent DV quantum circuits. This study aims to identify trade-offs between DV and CV paradigms for quantum-enhanced medical imaging. Our results highlight the potential of continuous-variable models and their viability for future computer-aided diagnosis systems.