ABSTRACT
In this chapter the authors propose a quantum digital twin model for health care by creating a process twin. The quantum digital twin could help in designing effective health care organs which would save lives. The organs are modeled according to their geometric structure. The vital parameters for the effective functionality of the organ are identified and their dependencies are modeled. A mathematical model is formulated based on these constraints. The boundary values for these parameters are decided. Quantum machine learning algorithms are used to effectively adjust these values. The quantum digital twin algorithm improves reliability by 33% and reduces processing time by 33%, compared to the conventional machine learning approaches.
