Privacy-preserving neutral atom-based quantum classifier towards real healthcare applications

TL;DR

This paper introduces a quantum classifier model that preserves data privacy by training on local data without transferring sensitive information to the cloud, demonstrating promising results on healthcare data using neutral atom quantum hardware.

Contribution

It presents a novel quantum SVM classifier reformulated as a QUBO problem, enabling privacy-preserving training on neutral atom quantum processors without data anonymization.

Findings

Effective classification on breast cancer dataset

Successful implementation on real neutral-atom QPU

Performance maintained under noisy simulation conditions

Abstract

Technological advances in Artificial Intelligence (AI) and Machine Learning (ML) for the healthcare domain are rapidly arising, with a growing discussion regarding the ethical management of their development. In general, ML healthcare applications crucially require performance, interpretability of data, and respect for data privacy. The latter is an increasingly debated topic as commercial cloud computing services become more and more widespread. Recently, dedicated methods are starting to be developed aiming to protect data privacy. However, these generally result in a trade-off forcing one to balance the level of data privacy and the algorithm performance. Here, a Support Vector Machine (SVM) classifier model is proposed whose training is reformulated into a Quadratic Unconstrained Binary Optimization (QUBO) problem, and adapted to a neutral atom-based Quantum Processing Unit (QPU). Our final model does not require anonymization techniques to protect data privacy since the sensitive data are not needed to be transferred to the cloud-available QPU. Indeed, the latter is used only during the training phase, hence allowing a future concrete application in a real-world scenario. Finally, performance and scaling analyses on a publicly available breast cancer dataset are discussed, both using ideal and noisy simulations for the training process, and also successfully tested on a currently available real neutral-atom QPU.