Decision Control Under Structured Uncertainty Using a Quantum-Inspired Density Matrix Framework

Machine learning systems are increasingly deployed in environments where decision errors carry significant consequences. In such settings, predictive probabilities alone often provide an incomplete basis for decision making, as they do not reveal why uncertainty arises or whether competing hypotheses are present. This limitation can lead to overconfident decisions, particularly when models encounter ambiguous or evolving conditions.This work introduces a quantum-inspired decision framework that represents predictive uncertainty using a density matrix formulation. Without relying on quantum hardware or computational assumptions, the proposed approach models structured interactions among hypotheses through off-diagonal terms in a normalized state representation. An ambiguity-aware adaptive threshold is derived from this structure and applied as a post-hoc decision layer, thereby regulating decisions without modifying or retraining the underlying predictive model.Theoretical analysis clarifies how structured ambiguity differs from scalar uncertainty measures, and controlled synthetic experiments illustrate its impact on decision boundaries and precision–recall behavior. Results show that the proposed method enables selective suppression of decisions in ambiguous regions while maintaining interpretability and controllability. By bridging uncertainty modeling with practical decision regulation, this framework contributes a simple and extensible mechanism for more reliable AI deployment in high-stakes applications.