Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery
This work addresses the challenge of discovering causal relationships in medicine with limited data, potentially enabling new medical knowledge discovery, though it appears incremental as it builds on the DirectLiNGAM algorithm.
The study applied quantum computing to a linear non-Gaussian acyclic model (qLiNGAM) for causal discovery from real-world medical data, demonstrating that it could correctly estimate causal structures with small data amounts where existing methods failed, as verified on IBM quantum hardware.
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.