用量子神经网络预测病人生命体征，提升小样本下的预测稳定性。
Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting
- 将量子电路嵌入GRU模型，用量子层混合多变量特征。
- 在15/30/60秒多步预测中表现优于传统方法，对缺失数据更鲁棒。
- 适合小样本临床场景，尤其缺数据时仍保持稳定性能。
预测生理信号可支持主动监测与及时临床干预，提前发现患者状态变化。本文针对心率、血氧饱和度、脉搏率和呼吸率的多变量多时域预测任务，设计了一种混合量子-经典架构：使用GRU编码器提取历史窗口的潜在表示，并将其映射为量子角度参数化变分量子电路（VQC）。量子层作为可学习的非线性特征混合器，在预测前建模变量间交互关系。在BIDMC PPG与呼吸数据集上采用留一患者排除法评估，结果表明该方法在准确率上媲美经典与深度学习基线，且对噪声和缺失输入具有更强鲁棒性。研究显示，混合量子层可在小样本临床设置中提供有益的归纳偏置。代码已公开于https://github.com/arco-group/quantum-ml。
原文摘要 · Abstract (English)
Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address multivariate multi-horizon forecasting of physiological time series by jointly predicting heart rate, oxygen saturation, pulse rate, and respiratory rate at forecasting horizons of 15, 30, and 60 seconds. We propose a hybrid quantum-classical architecture that integrates a Variational Quantum Circuit (VQC) within a recurrent neural backbone. A GRU encoder summarizes the historical observation window into a latent representation, which is then projected into quantum angles used to parameterize the VQC. The quantum layer acts as a learnable non-linear feature mixer, modeling cross-variable interactions before the final prediction stage. We evaluate the proposed approach on the BIDMC PPG and Respiration dataset under a Leave-One-Patient-Out protocol. The results show competitive accuracy compared with classical and deep learning baselines, together with greater robustness to noise and missing inputs. These findings suggest that hybrid quantum layers can provide useful inductive biases for physiological time series forecasting in small-cohort clinical settings. The code is available at https://github.com/arco-group/quantum-ml.
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