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

Abstract

Recently, the utilization of real-world medical data collected from clinical sites has been attracting attention. Especially as the number of variables in real-world medical data increases, causal discovery becomes more and more effective. On the other hand, it is necessary to develop new causal discovery algorithms suitable for small data sets for situations where sample sizes are insufficient to detect reasonable causal relationships, such as rare diseases and emerging infectious diseases. This study aims to develop a new causal discovery algorithm suitable for a small number of real-world medical data using quantum computing, one of the emerging information technologies attracting attention for its application in machine learning. In this study, a new algorithm that applies the quantum kernel to a linear non-Gaussian acyclic model, one of the causal discovery algorithms, is developed. Experiments on several artificial data sets showed that the new algorithm proposed in this study was more accurate than existing methods with the Gaussian kernel under various conditions in the low-data regime. When the new algorithm was applied to real-world medical data, a case was confirmed in which the causal structure could be correctly estimated even when the amount of data was small, which was not possible with existing methods. Furthermore, the possibility of implementing the new algorithm on real quantum hardware was discussed. This study suggests that the new proposed algorithm using quantum computing might be a good choice among the causal discovery algorithms in the low-data regime for novel medical knowledge discovery.