Quantum Geometry Learning Systems (QGLS) is a novel AI architecture that brings the principles of quantum mechanics into classical deep learning models through topological and entanglement-inspired design. Developed by Moonshot Labs, QGLS is fully open-source and released under the MIT License.
QGLS models intelligence as an emergent phenomenon of geometry, resonance, and entanglement, using knot-based structures, interference patterns, and wave-driven propagation. This architecture is a step toward topological intelligence systems shaped by the physics of quantum behaviorโwithout the need for quantum hardware.
- Entangled Connection Layer: Simulates interference using entanglement coefficients (ฮต), resonance phases (ฯ), and knot tension (ฯ).
- Topological Network Structure: Nodes are organized in trefoil or figure-eight knots to shape signal flow.
- Wave-Based Propagation: Information moves non-linearly across entangled paths.
- Collapse Resolution Layer: Resolves signal superposition using entropy, energy, or tension-based collapse mechanisms.
- Resonance Loss Function: Penalizes disharmonic phase interference to encourage coherent learning.
- Dataset Adapter: Maps classical input data onto the knot structure.
QGLS has shown competitive performance on Fashion MNIST with enhanced learning dynamics, high coherence, and smooth generalization under noise.
This project is released under the MIT License.
QGLS is a research project by Moonshot Labs, founded on the principle that AI should evolve through the shape of natureโs laws.
Research Paper: https://docs.google.com/document/d/1mZzgz7C_R4kewDWzKwi-aLm9-3jrW4ZVs1sSB0e76jg/edit?usp=sharing
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"Intelligence is not just learned โ itโs shaped."