Using Knowledge graph and Quantum Computing to Optimize the Comprehensive Mental Health Adaptive Test System

Xinhang Wang, Guangdi Liu, Le Zhang

Abstract: It is important for mental health research to study the scale of the elementary and secondary school students. Methods: This study uses knowledge graph technology to strengthen the relationship between individual psychological questionnaires and various test indicators of mental health, and we develop the lightweight extraction method "Relational database to Knowledge graph" (RETG) to generate the necessary files to build the knowledge graph. We not only employ quantum search algorithm to reduce the computational complexity, but also replace multi bit quantum gate in the quantum circuit by combining single- and two-bit quantum gate. Results: We built a scalable adaptive mental health testing platform for the elementary and secondary school students; The RETG method increases the efficiency of Knowledge graph construction; The quantum computing can reduce the time complexity of construction process and we increase the accuracy of quantum acceleration algorithms on physical machines.