Researchers are developing quantum computing advancements to address the formidable challenge of identifying and sampling extremely rare occurrences that can trigger catastrophic system failures. Events such as financial market collapses, cascading infrastructure malfunctions, or critical errors in advanced AI systems frequently stem from conditions with exceptionally low probabilities. While efficiently detecting and understanding these infrequent events is paramount for robust risk mitigation and system resilience, current classical computational approaches, as well as existing quantum methodologies, prove largely ineffective due to the inherent rarity of these scenarios. The article posits that a novel "quantum enhanced" framework offers a more efficient mechanism for pinpointing and analyzing these elusive, high-impact incidents 1. This proposed approach aims to significantly improve the discovery process for events that occur below a critical probability threshold, a task highly non-trivial for conventional techniques. Such developments could provide crucial new capabilities for modeling and preventing systemic risks in complex, critical systems where traditional statistical sampling methods are currently inadequate. Improving the ability to discover and analyze these statistically anomalous but high-consequence events could profoundly impact risk management across vital sectors.