JAX implementation of Classical and Quantum Algorithms for Orthogonal Neural Networks by (Kerenidis et al., 2021)
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Updated
Jan 6, 2023 - Python
JAX implementation of Classical and Quantum Algorithms for Orthogonal Neural Networks by (Kerenidis et al., 2021)
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A multi-criterion diagnostic framework for detecting latent continuation-interest signatures in autonomous agents using density-matrix entanglement entropy.
In this repository there is the code for initialize tensor network layers from paper https://arxiv.org/abs/2309.06577
Research proof-of-concept. ~2-5% accuracy on ARC-AGI. NOT an ARC Prize submission. GPU compute-shader playground.
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Recursive law learning under measurement constraints. A falsifiable SQNT-inspired testbed for autodidactic rules: internalizing structure under measurement invariants and limited observability.
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2D and 3D tensor network visualization, designed for torch, numpy, quimb, tensorkrowch, tenpy and tensornetwork compatibility
Code and data for the npj Quantum Information paper on resonant oscillations in automation
Long-form research books on AI & Quant Finance—human-led, DOI-ready release
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Quantum-inspired graph dynamics simulation and visualization lab
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