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 dynamical properties 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 via data ensembles from quantum simulations
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 technology stack assembles from low-level recoupling primitives up to full tensor network algorithms — each component supports the next, culminating in data that drives the Ideogenesis AI framework.
- Yuzuha CG Engine
SU(2) Clebsch–Gordan recoupling engine. Computes angular momentum coupling coefficients for symmetry-aware tensor operations.
Python - Nicole Tensor Library
Symmetry-aware tensor library for many-body systems. Provides block-sparse tensor objects that preserve quantum numbers during tensor operations.
Python - Alice TN Algorithms
1D tensor network methods built upon Nicole. Implements infrastructure and algorithms for ground state, finite-T and dynamical properties.
Python - Ideogenesis AI Framework
Transformer-based architectures trained on quantum lattice simulation data. Designed to acquire new physical insights by uncovering emergent correlations and latent structure beyond the reach of conventional analysis.
Private · Python
The Ideogenesis Framework
Built on top of the technology 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
Each component embodies living, collaborative open-source efforts. 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 block-sparse tensor objects that preserve quantum numbers during operations.
- Alice TN Algorithms
A collection of 1D tensor network algorithms built upon Nicole. Includes DMRG, (upcoming XTRG, tanTRG, TDVP, TaSK etc.) and MPS / MPO infrastructure.
- Yuzuha CG Engine
SU(2) recoupling engine for tensor network algorithms. Computes Clebsch–Gordan coefficients and Wigner symbols needed for symmetric tensor operations.
More repositories — including the private Ideogenesis Framework — live on the organization page.
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