This paper lays a theoretical foundation for Quantum Intelligence (QI): a framework of machine cognition, inspired not by the mechanics of classical computation, but by the principles of quantum mechanics– superposition, entanglement and non-determinism. This framework is distinct from quantum machine learning, which applies classical algorithms to quantum hardware. Quantum Intelligence instead refers to cognition that is quantum in structure and behavior. Unlike traditional AI, which relies on deterministic logic and statistical approximation, Quantum Intelligence proposes that intelligence itself may inherently be non-binary, probabilistic and contextually entangled– properties that cannot be fully captured by classical systems. This paper proposes an alternative framework for cognition, grounded in the principle that multiple interpretive states– such as beliefs, hypotheses, or conceptual configurations– can coexist in superposition. Rather than forcing premature resolution through probabilistic convergence, this framework enables dynamic reasoning through context-sensitive collapse. The resulting architecture models cognition not as deterministic computation, but as a structured interaction between ambiguity, observation, and coherence– offering a foundation for systems capable of interpretive depth and genuine cognitive flexibility.