标题： 纵向生物医学研究的量子机器学习框架 显示英文标题

标题： Quantum machine learning framework for longitudinal biomedical studies

摘要： 纵向生物医学研究在追踪疾病进展、治疗反应和耐药机制的出现方面发挥着至关重要的作用，尤其是在癌症和神经退行性疾病等复杂疾病中。 然而，生物数据的高维度与纵向队列规模有限相结合，给传统机器学习方法带来了重大挑战。 在本工作中，我们探讨了量子机器学习（QML）在纵向生物标志物发现中的潜力。 我们提出了一种对即时量子多项式时间（IQP）特征映射的新修改，旨在将多个时间点的时间依赖性编码到生物医学数据集中。 通过在合成和现实世界数据集上的数值模拟——包括滤泡性淋巴瘤和阿尔茨海默病的研究——我们证明了我们的纵向IQP特征映射提高了量子核捕捉受试者内时间模式的能力，为QML在临床研究中的应用提供了有前景的方向。 显示英文摘要

摘要： Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders such as cancer and neurodegenerative diseases. However, the high dimensionality of biological data, combined with the limited size of longitudinal cohorts, presents significant challenges for traditional machine learning approaches. In this work, we explore the potential of quantum machine learning (QML) for longitudinal biomarker discovery. We propose a novel modification to the instantaneous quantum polynomial time (IQP) feature map, designed to encode temporal dependencies across multiple time points in biomedical datasets. Through numerical simulations on both synthetic and real-world datasets - including studies on follicular lymphoma and Alzheimer's disease - we demonstrate that our longitudinal IQP feature map improves the ability of quantum kernels to capture intra-subject temporal patterns, offering a promising direction for QML in clinical research.