Stratified Quantum Normative Dynamics: A Unified Framework for Verifiable Ethical Reasoning in Autonomous Systems

We present , a comprehensive mathematical framework that unifies Stratified Geometric Ethics (SGE) with Quantum Normative Dynamics (QND). Where SGE provides the topological foundation for modeling moral discontinuities, threshold effects, and genuine dilemmas through stratified spaces, and QND extends classical ethicodynamics to capture superposition, entanglement, and measurement in moral reasoning, SQND synthesizes these approaches through the formulation. The key insight is that ethical ``energy'' must be conserved not only within smooth strata but also across the singular boundaries where moral phase transitions occur. We make seven principal contributions: (1) We formulate the Stratified QND Lagrangian _ governing dynamics within strata and at boundaries. (2) We introduce the ---the quantum of moral influence constrained by Whitney regularity. (3) We prove that the Bond Invariance Principle extends to quantum superpositions. (4) We establish that moral interference at boundaries is destructive for compounding wrongs, formally explaining why ``two wrongs don't make a right.'' (5) We derive finite approximation theorems with explicit error bounds for computational implementation. (6) We provide a complete threat model with diagnostic procedures. (7) We explore philosophical and theological implications of the unified framework. The theory makes testable predictions via quantum cognition experiments including stratification-enhanced order effects, boundary interference patterns, and contextuality signatures in collective responsibility judgments. SQND provides the mathematical foundation for real-time ethical governance in autonomous systems through the DEME 2.0 architecture. AI ethics, stratified spaces, quantum field theory, gauge invariance, formal verification, autonomous systems, moral philosophy, Whitney conditions Author preprint deposited for archival and citation. Draft — pending author review.