量子计算在线性非高斯无环模型中的应用：面向新型医学知识发现
Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery
AI总结：
本研究将量子计算应用于线性非高斯无环模型，利用量子核计算 DirectLiNGAM 的独立性度量，提出 qLiNGAM 方法。实验表明，在数据量较少时，qLiNGAM 能正确估计因果结构，并在真实量子硬件上实现，为小数据下的新型医学知识发现提供可能。
摘要：
近期，随着医学的数字化，利用从临床机构收集的真实世界医疗数据引起了广泛关注。本研究将量子计算应用于线性非高斯无环模型，以仅从真实世界医疗数据中发现因果关系。具体而言，使用量子核计算了因果发现算法 DirectLiNGAM 的独立性度量，并验证了其在真实世界医疗数据上的准确性。当带有量子核的 DirectLiNGAM（qLiNGAM）应用于真实世界医疗数据时，证实了在数据量较少的情况下能够正确估计因果结构的案例，这是现有方法无法做到的。此外，在使用 IBMQ 的实验中，qLiNGAM 被部署在真实量子硬件上。结果表明，即使只有少量可用数据，qLiNGAM 也有可能发现新的医学知识并为解决医疗问题做出贡献。
英文摘要：
Recently, with the digitalization of medicine, the utilization of real-world medical data collected from clinical sites has been attracting attention. In this study, quantum computing was applied to a linear non-Gaussian acyclic model to discover causal relationships from real-world medical data alone. Specifically, the independence measure of DirectLiNGAM, a causal discovery algorithm, was calculated using the quantum kernel and its accuracy on real-world medical data was verified. When DirectLiNGAM with the quantum kernel (qLiNGAM) was applied to real-world medical data, a case was confirmed in which the causal structure could be correctly estimated when the amount of data was small, which was not possible with existing methods. Furthermore, qLiNGAM was implemented on real quantum hardware in an experiment using IBMQ. It is suggested that qLiNGAM may be able to discover new medical knowledge and contribute to the solution of medical problems, even when only a small amount of data is available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
