Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery

TL;DR

This paper explores the use of quantum computing to enhance causal discovery in medical data, demonstrating improved accuracy with small datasets and implementation on real quantum hardware.

Contribution

It introduces qLiNGAM, a quantum kernel-based causal discovery method, showing its effectiveness on real-world medical data and hardware.

Findings

qLiNGAM accurately estimates causal structures with limited data

Implementation of qLiNGAM on IBMQ hardware demonstrated feasibility

qLiNGAM has potential for discovering new medical knowledge

Abstract

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.