In this work, I introduce the H-ImmQπDecoder v2.0, a new class of quantum error decoder that integrates three novel mechanisms: an immune-inspired adaptive memory capable of learning, mutating, and generating new local correction strategies, a dynamic π-field representing spatio-temporal quantum coherence and noise topology, a Quantum Metabolism regulating the decoder’s adaptive behavior through an internal energy function. In addition, the decoder implements a non-linear Quantum-π Phase Correction mechanism that operates beyond discrete Pauli corrections by applying continuous phase alignment driven by π-field gradients. To my knowledge, no existing quantum decoder employs a bio-inspired immune system, a hydrodynamic π-field, and metabolic feedback simultaneously. The architecture presented here is therefore fundamentally new, and I formally claim its novelty and authorship. The proposed decoder is designed to handle correlated, non-Markovian, or topologically structured noise, situations where conventional decoders (MWPM, BP, NN-based) rapidly degrade. I provide the conceptual framework, usage conditions, algorithmic details, and practical motivation for real quantum processors. Keywords: quantum error correction (QEC), immune-inspired algorithms, π-field dynamics, continuous phase correction, quantum metabolism, adaptive decoding, correlated noise.