We propose a scientific framework for the phenomenological state of Samadhi, modeling it as a systematic, computational process of "de-coarse-graining." We posit that human consciousness operates as a "highly coarse, asymmetric renderer" of information (termed the Human Neural Network, or HNN), akin to autonomous intelligence models that predict states by omitting high-frequency details. This rendering, an evolutionary optimization for survival, is achieved via a "symmetry cascade"-a series of spontaneous symmetry breakings (SSB) that generate a finite, localized, and dualistic (subject-object) reality from a nonlocal, non-dual, and symmetric informational ground state. The computational "cost" of this coarse-graining is an informational deficit, or a "drift from the truth," which can be modeled as a subjective loss function (  ). We hypothesize that Samadhi is an algorithmic procedure to systematically reverse this cascade, sequentially "un-breaking" these symmetries. This process computationally reduces the informational loss (  ), allowing the HNN to access the "ultimate totality of truth"-the truly symmetric, informationally complete ground state of the underlying computational substratum. This model provides a falsifiable prediction: the de-coarse-graining algorithm requires the HNN to access the quantum properties of its foundational layer, which would manifest as anomalous, sustained quantum coherence in the neural substrate, actively protected by a Universal Quantum Error Correction (UQEC) mechanism.