Synthetic Comparative Analysis of Cardiovascular Disease Profiles Using Quantum Information Measures in Indian and Global Populations

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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 (ρ) 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 ≈ 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.

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