Hi everyone,

Over the past few months, I’ve been working on a new library and research paper that unify structure-preserving matrix transformations within a high-dimensional framework (hypersphere and hypercubes).

Today I’m excited to share: MatrixTransformer a Python library and paper built around a 16-dimensional decision hypercube that enables smooth, interpretable transitions between matrix types like

• Symmetric
• Hermitian
• Toeplitz
• Positive Definite
• Diagonal
• Sparse
• ...and many more

It is a lightweight, structure-preserving transformer designed to operate directly in 2D and nD matrix space, focusing on:

• Symbolic & geometric planning
• Matrix-space transitions (like high-dimensional grid reasoning)
• Reversible transformation logic
• Compatible with standard Python + NumPy

It simulates transformations without traditional training—more akin to procedural cognition than deep nets.

What’s Inside:

• A unified interface for transforming matrices while preserving structure
• Interpolation paths between matrix classes (balancing energy & structure)
• Benchmark scripts from the paper
• Extensible design—add your own matrix rules/types
• Use cases in ML regularization and quantum-inspired computation

Links:

Paper: https://zenodo.org/records/15867279
Code: https://github.com/fikayoAy/MatrixTransformer
Related: [quantum_accel]—a quantum-inspired framework evolved with the MatrixTransformer framework link: fikayoAy/quantum_accel

If you’re working in machine learning, numerical methods, symbolic AI, or quantum simulation, I’d love your feedback.
Feel free to open issues, contribute, or share ideas.

Thanks for reading!