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

• URL: http://arxiv.org/abs/2505.04570v1
• Date: Wed, 07 May 2025 17:03:35 GMT
• Title: Privacy-preserving neutral atom-based quantum classifier towards real healthcare applications
• Authors: Ettore Canonici, Filippo Caruso,
• Abstract summary: ML healthcare applications crucially require performance, interpretability of data, and respect for data privacy.<n>Recently, dedicated methods are starting to be developed aiming to protect data privacy.<n>Here, a Support Vector Machine (SVM) classifier model is proposed whose training is reformulated into a Quadratic Unconstrained Binary Optimization problem.<n>The 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.
• Score: 1.2891210250935148
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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