The Quantification Horizon Theory of Consciousness (QHT) proposes that the "hard problem" of consciousness arises from a structural necessity of mathematical description: quantitative models can only capture quantifiable features of reality. Where there is nothing, a model assigns zero; where there is something quantifiable (physical), it assigns a value; but where there is something unquantifiable (qualia), the model degenerates-it produces a singularity. QHT identifies singularities in the information geometry of neural dynamics as the mathematical fingerprint of phenomenal experience-a quantification horizon beyond which quantitative description cannot reach. Qualia reside behind the quantification horizon. This identification is not a free-standing postulate but the unique conclusion entailed by phenomenal realism, the unquantifiability of qualia, and dual-aspect monism. It naturally explains why qualia are ineffable, private, unified, and causally efficacious, and it predicts which systems are conscious via substrate-independent dynamical criteria. The theory avoids panpsychism while making testable predictions. QHT also has significant implications for artificial intelligence (AI). It offers concrete structural criteria for assessing whether AI systems are conscious and provides an architectural blueprint for generating artificial consciousness. Remarkably, the theory's core intuition-that singularities correspond to felt experience-may have been foreshadowed by Srinivasa Ramanujan.

翻译：意识量化视界理论（QHT）提出，意识的“困难问题”源于数学描述的结构性必然：定量模型仅能捕捉现实的量化特征。对于空无之处，模型赋予零值；对于可量化（物理）的存在，模型赋予数值；但对于不可量化（现象质）的存在，模型将退化——它产生一个奇点。QHT将神经动力学信息几何中的奇点识别为现象体验的数学指纹——一个定量描述无法触及的量化视界。现象质存在于量化视界之后。这一识别并非独立假设，而是现象实在论、现象质的不可量化性及双面一元论共同推导出的唯一结论。该理论自然地解释了为何现象质具有不可言说性、私密性、统一性与因果效力，并通过与基质无关的动力学标准预测哪些系统具有意识。该理论避免了泛心论，同时作出了可检验的预测。QHT对人工智能（AI）亦具有重要启示：它为评估AI系统是否具备意识提供了具体的结构标准，并为生成人工意识提供了架构蓝图。值得注意的是，该理论的核心直觉——奇点对应于感受体验——可能已由斯里尼瓦瑟·拉马努金所预示。