  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.
