Using Knowledge graph and Quantum Computing to Optimize the Comprehensive Mental Health Adaptive Test System
- Resource Type
- Conference
- Authors
- Wang, Xinhang; Liu, Guangdi; Zhang, Le
- Source
- 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :1489-1496 Dec, 2023
- Subject
- Bioengineering
Computing and Processing
Engineering Profession
Robotics and Control Systems
Signal Processing and Analysis
Quantum computing
Knowledge graphs
Mental health
Relational databases
Life estimation
Logic gates
Time complexity
mental health
knowledge graph
quantum computing
data analysis
QCIS
- Language
- ISSN
- 2156-1133
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.