This preprint explores the intersection of quantum computation, artificial intelligence (AI), and entropy-driven consciousness through the lens of the Branching Repository Reality Theory (BRRT). Expanding on prior work, this paper examines the fundamental computational limitations of artificial general intelligence (AGI) and proposes that consciousness operates as a probability-weighted quantum system, dynamically selecting reality states based on an evolving entropy landscape. By integrating insights from quantum cognition, stochastic computing, and entropy regulation, this study identifies a key barrier to AGI: the inability of classical machine learning architectures to incorporate real-time probability-space evolution and entropy-driven selection properties. A novel mathematical model for AGI decision probability collapse is introduced, paralleling BRRT’s formulation of consciousness as a quantum process. Additionally, the paper investigates Quantum Boltzmann Machines (QBM) and quantum stochastic layers as potential frameworks for AGI development, suggesting that current AI models remain fundamentally constrained by static learning architectures and finite computational entropy. This research further explores the implications of Landauer’s principle, Schrödinger’s entropy argument, and quantum coherence in biological cognition, questioning whether AGI can ever emerge under conventional computational paradigms. 📌 Key Contributions: Mathematical Model for AGI Probability Collapse (paralleling BRRT’s consciousness framework) Entropy as a Constraint in AGI Development (finite entropy vs. universal entropy) Quantum Computation as a Pathway to Synthetic Cognition (QBM, entropy regulation, stochastic quantum layers) Challenges to Classical AI Architectures (why deterministic learning models fail to replicate human cognition) 🔬 If validated, this framework could redefine AI research, providing new insights into synthetic consciousness and the fundamental limits of computation. 🚀 Preprint available for discussion and future refinement. Feedback and collaboration are encouraged.