量子特征选择用于生物医学数据分析

中文摘要 AI 辅助

特征选择是降低高维数据复杂性的关键步骤，通常为开发机器学习模型（如计算生物标志物）做准备。然而，特征选择存在局限性，例如在代谢组学数据中，每个研究参与者有数百甚至数千个特征，而临床试验中可用的参与者数量有限。经典方法如穷举搜索需要评估所有可能的特征组合，随着特征维度的增加，这在计算和运行时间方面成本高昂，甚至不可行。在本研究中，我们提出了一种新颖的二次无约束二元优化（QUBO）系数公式，并将代谢组学特征选择表述为一个QUBO问题，通过平衡所选变量之间的相关性与冗余性来选择指定数量的特征。为了评估我们提出的QUBO目标函数，我们在基于量子门的计算机上使用偏置场数字化的反绝热量子优化（BF-DCQO）和量子近似优化算法（QAOA）进行了一系列实验。我们还将该方法与几种经典方法在三个与自闭症谱系障碍（ASD）相关的代谢组学数据集上进行了比较，这些比较在经典计算机上进行。与穷举搜索和迭代禁忌搜索（ITS）相比，我们的方法减少了运行时间，并且在分类器上取得了与经典过滤、包装和嵌入方法相竞争的性能。这些结果并不声称量子优势；相反，它们确立了硬件可行性，并展示了嘈杂中等规模量子（NISQ）设备的当前能力。

英文摘要

Feature selection is an essential step for reducing complexity of high dimensional data, usually in preparation for developing machine learning models such as computational biomarkers. However, there are limitations associated with feature selection such as for metabolomic data where there are hundreds or even thousands of features per study participant, while the available number of participants in a clinical trials is limited. Classical methods such as exhaustive search require evaluation of all possible feature combinations, making them costly in terms of computation and runtime, or even infeasible, as feature dimensionality increases. In this study, we propose a novel Quadratic Unconstrained Binary Optimization (QUBO) coefficient formulation and pose metabolomic feature selection as a QUBO problem that selects a specified number of features by balancing their relevance against redundancy among the selected variables. To evaluate our proposed QUBO objective function, we conducted a series of experiments using Bias-Field Digitized Counterdiabatic Quantum Optimization (BF-DCQO) and Quantum Approximate Optimization Algorithm (QAOA) on a quantum gate based computer. We also compared the method to several classical methods on three metabolomic datasets associated with Autism Spectrum Disorder (ASD) on a classical computer. Our method reduces runtime compared with exhaustive search and Iterative Tabu Search (ITS) and achieves competitive performance across classifiers compared to classical filter, wrapper, and embedded methods. These results do not claim quantum advantage; rather, they establish hardware feasibility and demonstrate the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices.

发表机构

• Rensselaer Polytechnic Institute（伦斯勒理工学院）
• Kipu Quantum GmbH（Kipu Quantum 有限公司）

机构由 AI 辅助整理，请以论文原文为准。