This white paper introduces the Hybrid Neural Quantum Architecture (HNQA) — a theoretical framework that integrates deterministic neural learning with quantum-inspired probabilistic state encoding. HNQA proposes a dual-layer system: a classical deterministic core coupled with a probabilistic amplitude layer capable of representing multiple potential states simultaneously. The architecture models cognitive processes such as perceptual ambiguity and contextual collapse, aiming to enable artificial systems that learn from uncertainty rather than minimizing it.The document outlines the conceptual structure, mathematical rationale, and potential applications across adaptive AI governance, cybersecurity, cognitive simulation, and hybrid computation. It further discusses implementation challenges, energy efficiency, and integration paths with existing deep-learning frameworks.