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!