arXiv cs.AI· Md Khalid Syfullah, Alvi Ataur Khalil·· 13 小时前AI 评分22
面向临床与可穿戴健康数据的单位感知差分隐私量子机器学习
Unit-Aware Differentially Private Quantum Machine Learning for Clinical and Wearable Health Data
AI 导读
研究在匹配输入维度和隐私预算下，量子模型是否比经典模型保留更多效用：在三个临床队列和一个可穿戴队列上对比容量匹配的量子模型、多层感知机与逻辑回归，量子模型在三个临床队列上平均 ROC AUC 最高领先 0.104，但在参与者较少的可穿戴队列上落后，24 项检验经 Holm 校正后均无显著差异。
正文 · 原文
Abstract:Clinical registries and wearable sensors support early health-risk assessment, yet models trained on these records may leak sensitive information when released. Differential privacy (DP) bounds this leakage by injecting noise whose total magnitude grows with the number of trainable parameters, a cost especially severe for small health cohorts. This motivates parameter efficient models including quantum machine learning (QML), whose variational circuits can represent complex decision boundaries with few trainable parameters. It raises a question: at matched input dimensionality and privacy budget, does a quantum model retain more utility than a classical one? We investigate this question on three clinical cohorts and one wearable cohort, treating each clinical record or participant as a privacy unit. We compare capacity-matched quantum, multilayer perceptron, and logistic regression models across five privacy budgets and five seeds. The quantum model leads on three clinical cohorts by up to 0.104 ROC AUC averaged across budgets; however, trails on the wearable cohort, where the small number of participants requires substantial noise. No difference survives Holm correction across 24 tests, so these results indicate a trend rather than a quantum advantage. An analytic gradient bound shows that lower clipping thresholds can improve utility by reducing noise despite clipping bias. Finally, an experiment on IBM quantum computer (ibm_fez, a 156-qubit superconducting processor) using eight fixed test cases yields mean absolute probability errors of 0.046 (private) and 0.063 (non-private) relative to simulation, supporting the feasibility of inference on noisy intermediate-scale quantum (NISQ) hardware. Code is publicly available at this https URL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2610.28968 [cs.LG] |
| (or arXiv:2610.28968v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28968 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Md Khalid Syfullah [view email]
[v1]
Tue, 20 Oct 2026 22:38:13 UTC (347 KB)
来源：arXiv cs.AI · arxiv.org