在基因表达数据上验证了量子核方法的分类优势
Empirical Quantum Advantage Analysis of Quantum Kernel in Gene Expression Data
- 将量子线路嵌入核方法，用于基因表达数据分类
- 实验证明量子模型在特定数据上表现优于经典模型
- 为生物医学领域的复杂分类问题提供新思路
将量子变分线路融入机器学习分类模型，展现了从数据中提取模式进行分类的能力。然而，利用量子机器学习的计算优势需应对诸多挑战。本文聚焦于寻找可实现量子优势的数据集、评估经典与量子方法选择特征的相关性，并通过基准测试对比量子与经典方法，估算量子电路的计算复杂度以评估实际可用性。实验选用基因表达数据集，因其在调控生理行为和疾病易感性中的关键作用。本研究旨在推动量子机器学习方法的发展，为多个领域解决复杂分类问题提供有价值的洞见。
原文摘要 · Abstract (English)
The incorporation of quantum ansatz with machine learning classification models demonstrates the ability to extract patterns from data for classification tasks. However, taking advantage of the enhanced computational power of quantum machine learning necessitates dealing with various constraints. In this paper, we focus on constraints like finding suitable datasets where quantum advantage is achievable and evaluating the relevance of features chosen by classical and quantum methods. Additionally, we compare quantum and classical approaches using benchmarks and estimate the computational complexity of quantum circuits to assess real-world usability. For our experimental validation, we selected the gene expression dataset, given the critical role of genetic variations in regulating physiological behavior and disease susceptibility. Through this study, we aim to contribute to the advancement of quantum machine learning methodologies, offering valuable insights into their potential for addressing complex classification challenges in various domains.
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