Ideogenesis AI
Acquiring new physical insights via AI algorithms on ensembles
of data from quantum lattice simulations
- Symmetry-Exact Tensors Abelian and non-Abelian quantum numbers conserved exactly across all tensor operations
- Tensor Network Methods Ground state, finite temperature, and real-time dynamics for quantum many-body systems
- Quantum Lattice Models Spin chains, Hubbard models, and frustrated geometries spanning 1D to quasi-2D
- AI-Driven Discovery Emergent physical correlations uncovered from quantum simulation ensembles
A New Paradigm for Physical Research
Ideogenesis AI is a research organization dedicated to bridging the gap between traditional theoretical physics and modern AI capabilities. We believe that breakthrough discoveries emerge not only from derivations, but also from observations — direct patterns and correlations extracted from numerical or experimental simulations.
Our work centers on strongly correlated quantum many-body systems. We build the computational infrastructure — tensor network libraries, symmetry engines, and simulation algorithms — that produces the high-quality data our AI framework learns from.
From Simulation to Discovery
The simulation stack flows upward — each layer feeds the next, culminating in data that trains the Ideogenesis AI framework.
- Yuzuha CG Engine
SU(2) Clebsch–Gordan recoupling engine. Computes angular momentum coupling coefficients for symmetry-aware tensor contractions.
Python - Nicole Tensor Library
Symmetry-aware tensor library for many-body systems. Provides SU(2)-invariant tensor objects that preserve quantum numbers exactly.
Python - Alice TN Algorithms
1D tensor network algorithms built on Nicole. Implements DMRG, MPS, and MPO-based methods for ground state and dynamics.
Python - Ideogenesis AI Framework
Transformer-based architectures trained on simulation data to discover emergent correlations and physical patterns.
Private · Python
The Ideogenesis Framework
Built on top of the simulation stack, the Ideogenesis Framework provides a comprehensive suite of transformer-based architectures and analysis tools for discovering emergent physics from data.
State-of-the-art transformer architecture optimized for lattice system analysis, featuring novel attention mechanisms with locality biases and modular Processor/Propagator/Attention building blocks.
Advanced model diagnostics implementing attention propagation analysis, carrier transitions, Markov spectrum computation, and Omnimetry — a statistical framework for measuring physical observables.
Comprehensive visualization that transforms complex model outputs into intuitive representations, with TensorBoard integration and specialized attention visualization tools.
A Homebrew-inspired resource management system that streamlines handling of datasets, models, and experimental artifacts — with centralized registry management, automatic versioning, and dependency resolution.
Pre-trained models and curated datasets available on Hugging Face — lattice systems, quantum simulations, and complex dynamical data.
Tensor Network Simulations
The technology stack is entirely open source. Together, our projects cover the ground from symmetry bookkeeping to quantum simulation algorithms.
- Nicole Tensor Library
A symmetry-aware tensor library for many-body quantum systems. Implements SU(2)-invariant tensor objects that preserve quantum numbers exactly during contractions.
- Alice TN Algorithms
A collection of 1D tensor network algorithms built upon Nicole. Includes DMRG, MPS, and MPO methods for ground state computation and time evolution.
- Yuzuha CG Engine
SU(2) recoupling engine for tensor network algorithms. Computes Clebsch–Gordan coefficients and Wigner symbols needed for symmetric tensor contractions.
More repositories — including the private Ideogenesis Framework — live on the organization page.
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