An experimental, quantum-inspired, sparse holographic AI inference engine.
ThereminQ HoloQubed is a radical departure from traditional dense neural network execution. Instead of relying on brute-force, dense matrix multiplications ($O(n^2)$) that bottleneck on compute cores and PCIe bandwidth, Holoqubed leverages high-speed memory bandwidth, $O(1)$ spatial coordinate lookups, and bit-interleaved geometric encoding to perform AI inference.
Currently in the prototyping phase, the engine is built in Python (PyOpenCL,Llama.cpp + future Weed implementations ) to map the mathematical abstractions before being ported to bare-metal C++ for absolute maximum PCIe Zero-Copy efficiency.
Core Architecture
Traditional Large Language Models (LLMs) push massive weight matrices across the PCIe bus for every single token. HoloQubed bypasses this by translating neural pathways into physical memory space:
The Holographic Dictionary: Stored in massive system RAM (e.g., 320GB). It maps token pathways as spatial coordinates rather than dense weights.
Spatial Encoding (Holoqubed Research Logic): Converts floating-point activation thresholds into 1D spatial coordinates using a custom bit-interleaving scheme (bitwise XOR and shifts), producing a Z-order (Morton) curve.
Tesseract KV Cache: Represents a 4D coordinate space mapping the active sequence generation.
Sparse Execution: Resolves $O(\log N)$ or $O(1)$ lookups on the CPU and only pushes active coordinate pathways across the PCIe bus to the GPU. This effectively neutralizes traditional PCIe bottlenecks, allowing the engine to run over x4 connections.
