QSOLAI Quantum-Sourced Optimization Logic — AI Kernel
A QSOL-IMC Research Framework by Trent Slade
Overview
QSOLAI is the core AI kernel of the QSOL-IMC ecosystem — a modular, deterministic, minimal-dependency framework for exploring:
Quantum Error Correction (QEC)
Unified Field Theory (UFT) prototypes
Qutrit encoders
Spectral algebraics & audio DSP
Sonification templates
Pattern-logic, memetics, and information vortices
This project is designed for researchers, tinkerers, artists, theorists, and scientists who want fast iteration, reproducible runs, and clean architecture without heavy frameworks or bloated dependencies.
Small is beautiful. Fast is holy. Deterministic by default.
Features Core Architecture
Modular structure: encoder/, runner/, sonifier/, analysis/.
Single-seed deterministic execution.
Lightweight dependency footprint.
CLI interface for experiments and sonification runs.
Clean JSON/YAML configuration workflow.
Quantum + DSP + Memetics
Qutrit encoder with upcoming unit tests.
Quantum-inspired logic mapping (QSOL signatures).
Sonification module for audio-based data exploration.
Research templates for UFT/TFT integration.
Memetic logic tools (pattern detection, embedding prep).
Reproducibility
Each run generates:
metadata.json
Seed logs
Audio/visual outputs (if enabled)
Experiment snapshots
Repository Structure QSOLAI/ ├── encoder/ # Qutrit encoder, transformations, unit tests (in progress) ├── runner/ # Deterministic run engine, CLI entrypoint ├── sonifier/ # Audio DSP tools, templates, spectral algebraics ├── analysis/ # Logs, metadata exporters, summaries ├── config/ # experiment.yml / config.yml templates ├── assets/ # diagrams, architecture images ├── tests/ # pytest unit tests ├── README.md # You are here └── requirements.txt # Minimal Python deps
Installation Prerequisites
Python 3.11+
ffmpeg (required for sonification output)
Git
Clone & Setup git clone https://github.com/QSOLKCB/QSOLAI.git cd QSOLAI
python -m venv venv source venv/bin/activate # macOS/Linux
pip install --upgrade pip pip install -r requirements.txt
Quick Start
- Configure an Experiment
Edit config/config.yml or use the provided template:
seed: 42 mode: run sonification: true output_dir: results/ template: default qutrit_encoder: true
-
Run the System python runner/runner.py --config config/config.yml
-
View Outputs
After running, you’ll find:
results/sonification.wav — audio output
results/log.txt — full deterministic log
results/metadata.json — seed, versioning, module info
results/data.npz — encoded arrays, model outputs (if enabled)
Deterministic Execution
QSOLAI uses explicit seeds at all stages:
seed: 42
This ensures:
identical output across machines
reproducibility for scientific workflows
deterministic sonification
repeatable QEC/QSOL logic routines
All modules must respect this seed constraint. (Contributors: don’t introduce nondeterminism without a clear switch.)
Modules Encoder
Qutrit encoding
Tensor transformations
Basis cycling
(Upcoming) Full unit tests for deterministic mapping
Runner
Main orchestrator
Loads config
Applies global seed
Manages module order
Handles logging + reproducibility
Sonifier
Audio-DSP pipeline
Spectral algebraics tools
Standardized sonification templates
Generates .wav or .flac output
Optional spectral visualizations
Analysis
Metadata export
Result summaries
Run comparisons
Seed verification utilities
Dependencies
Minimal, clean Python stack:
numpy
scipy
librosa (if audio enabled)
soundfile
pyyaml
pytest (dev)
System requirement:
ffmpeg
Roadmap (v1 → v2)
Complete qutrit encoder test suite
Add deterministic “v1 runner” finalization
Standardize sonification templates
Integrate UFT/TFT experimental modules
Metadata indexing for multi-run comparison
CI pipeline with reproducibility checks
Optional Rust backend for ultra-minimal builds
Contributing
PRs are welcome — but respect the following:
No bloat. Every dependency must justify itself.
Determinism first. All randomness must use the global seed.
Modular code. No dumping everything into runner.py.
Readable logic > clever magic. Future you should understand current you.
License
MIT License. See LICENSE for full text.
Citation
When referencing this repository in academic or research work:
Slade, T. (2025). QSOLAI: Quantum-Sourced Optimization Logic AI Kernel.
QSOL-IMC Research Group. GitHub: https://github.com/QSOLKCB/QSOLAI
DOI: (Zenodo DOI pending)
Acknowledgements
This project is part of the growing QSOL-IMC universe, including:
QEC — Quantum Error Correction Framework
UFT — Unified Field Theory
Spectral Algebraics
Dark-Country Industrial Sonification Series
QSOL Synth, LOSTSOUND, QNToy
AI-assisted development supported through iterative research dialogue with ChatGPT.