We present AKNN (Aaryan's / Associative K-Nearest-Neighbor Neural Network), a dual-substrate neuro-manifold architecture for associative knowledge storage and retrieval. AKNN encodes natural language into two parallel geometric substrates simultaneously: an 8-qubit parametric quantum circuit producing PauliZ expectation signatures in a 256-dimensional Hilbert space, and a 10,000-dimensional bipolar Hyperdimensional Computing (HDC) vector space. Retrieval operates via manifold proximity in the quantum substrate, gated by HDC cosine similarity and a semantic coherence filter. A Hebbian plasticity layer continuously modulates memory strength with each access. An offline cognitive maintenance cycle performs Ebbinghaus decay, geometric clustering, pruning, and memory crystallization. Dynamic neurogenesis auto-spawns specialist expert neurons when tag-density thresholds are exceeded. The system is evaluated on a hand-curated DNA knowledge base of 54 memories across three cortical columns (BROTHERHOOD, ARCHITECT, ENTITY) running on Google Colab CPU. The paper documents all formal mathematics, the complete pipeline architecture, and retrieval results comparing quantum-only versus HDC-boosted tag-priority retrieval. Keywords: neuro-manifold, hyperdimensional computing, quantum encoding, associative memory, Hebbian learning, IIT proxy, knowledge retrieval, cortical columns, neurogenesis.
Aaryan Khan (Thu,) studied this question.