A Unified Quantum-Geometric Framework for Generalized Artificial Neural Networks

We present a unified framework integrating Loop Quantum Gravity (LQG) and Quantum Field Theory (QFT) to model abstract Artificial Neural Networks (ANNs). Existing ANNs lack a physical basis for higher-order cognition, self-reference and general intelligence, motivating an LQG- and QFT-based framework. We model neurons as quanta of space at LQG spin-network nodes, neuron states as quantum states in Hilbert space and edges labeled by spins serving as complex spin-weights defining the network topology. Information processing is described as quantum-state evolution governed by Hamiltonian constraints of LQG, where learning consists of discrete, unitary, energy-conserving updates, reducing curvature and encoding information as geometric deformation. QFT-based input and output layers are modeled as continuous field excitations that interact coherently with the discrete spin-network geometry. Spin transitions yield gradient-like updates linking optimization to curvature flow in quantum geometry. Higher-order dynamical feedback states emerge that encode both external data and the network’s own evolution, enabling self-referential computation. Conserved excitations interacting as particles in QFT drive the unitary evolution that sustains memory, suggesting a unified ANN framework.