Santosh Kumar Sahoo

Background/Objectives: Cardiovascular diseases (CVDs) represent the foremost public health challenge of recent days. According to the World Health Organization (WHO), nearly 32% of people die from CVDs each year. This paper introduces a quantum-inspired, information-theoretic framework for comparing CVD risk-factor architectures in modeled Indian and global populations. Methods: Each synthetic population is represented as a population state matrix ($\rho$) built from discretized epidemiological variables (age, sex, smoking, diabetes, hypertension, and dyslipidemia). Systemic uncertainty is quantified with von Neumann entropy (equivalent to Shannon entropy for diagonal states), inter-factor dependence with Systemic Mutual Information (SMI), and synthetic population similarity with Uhlmann fidelity. Results: In illustrative modeled scenarios ($N=10,000$ synthetic profiles per synthetic population), diabetes entropy is higher in the Indian comparator ($0.88$ vs. $0.61$ bits), diabetes–hypertension SMI is larger in India (≈0.46 vs. $0.22$ bits), and smoking–hypertension dependence is stronger globally ($0.36$ vs. $0.21$ bits). Overall fidelity is $F\approx 0.76$. Conclusions: These results are hypothesis-generating point estimates and require clinical data verification on harmonized synthetic individual-level data before clinical or policy use. This study represents a quantum information-theoretic framework for comparing modeled CVD risk profiles. Illustrative results suggest stronger diabetes–hypertension dependence in the Indian scenario and stronger smoking–hypertension dependence in the global comparator.