Neuromorphic Quantum Computing

A 3D Brain-Inspired CPU with Schrödinger Qutrits & QTUN

[Python 3.9+] [CUDA Recommended] [Plasticity ON]

Prefers NVIDIA GPU (CUDA), with automatic CPU retry on CUDA OOM Full 3D volumetric learning with synaptic plasticity

Live Demo: 2+ Million Neuron 3D Brain

python3 brain3d.py --grid 128 --strong-input --3d-inputs

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[3D volumetric learning plot will be added]

Output:

• 128³ grid → 2,097,152 neurons, 53.6M edges
• Plasticity ON (default)
• STRONG input on full 3D sheets
• Volumetric learning across all layers
• Weight change: +214M (learning confirmed)
• VRAM: ~3.4 GB peak

Why This Project?

Feature	Benefit
3D Neuromorphic CPU	Mimics human volumetric processing
Synaptic Plasticity	Real-time learning (default ON)
CUDA-Accelerated	128³ grid in 73 seconds
Qutrits + QTUN	Quantum-enhanced control (in cartpole_a2c.py)

Installation

Prerequisites

• Python 3.9 or higher
• NVIDIA GPU with CUDA support (recommended for large networks)
• 16 GB+ RAM recommended

Setup

1. Clone the repository:

git clone https://github.com/EdgeOfAssembly/neuromorphic-quantum-computing.git
cd neuromorphic-quantum-computing

2. Install dependencies:

pip install -r requirements.txt

3. For GPU support, install PyTorch with CUDA:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Verify Installation

python3 -c "import torch; print('CUDA available:', torch.cuda.is_available())"

Quick Start (Full Experience)

# Requires: NVIDIA GPU + CUDA + PyTorch
python3 brain3d.py --grid 128 --strong-input --3d-inputs

# Explicit device selection
python3 brain3d.py --grid 64 --device cpu
python3 brain3d.py --grid 64 --device auto

For smaller test:

python3 brain3d.py --grid 64 --no-plasticity

--device auto and --device cuda both try CUDA first and retry once on CPU if CUDA runs out of memory. --device cpu skips CUDA entirely.

Key Programs

• brain3d.py --- 3D neuromorphic CPU with plasticity
• cartpole_a2c.py --- QTUN + A2C on CartPole (quantum control)
• sim_basic_qutrit.py --- Qutrit neuron demo (CPU-only)
• benchmark_brain3d.py --- Comprehensive performance benchmarking suite
• demo_benchmark.py --- Quick benchmark demonstration

Research Documents

• brain3d.pdf --- Detailed architecture and implementation documentation
• qtun.pdf --- Quantum Tunneling Unit Neuron (QTUN) model specification
• comp16.pdf --- Computational architecture and design principles

For implementation details, see IMPLEMENTATION_SUMMARY.md
For code review findings, see CODE_REVIEW_FINDINGS.md

Roadmap

• 128³ 3D brain with volumetric learning
• Synaptic plasticity (default ON)
• CUDA acceleration
• CPU fallback mode
• QTUN full integration
• Real-time visualization
• Research paper

Detailed completed work and future experiment backlog are tracked in TODO.md.

Contributing

See CONTRIBUTING.md

Good first issues:

• Add 3D spike visualization (brain3d.py)
• Improve OOM regression tests for CLI fallback
• 1-page paper: "3D Neuromorphic Volumetric Learning"

Hardware Requirements

Component	Required
GPU	NVIDIA with CUDA (4GB+ VRAM)
RAM	16 GB+ recommended
Storage	500 MB

Contributors

[You could be here!]

License

This repository is dual-licensed.

For non-commercial use, this project is licensed under the GNU General Public License v3.0. Please see the LICENSE file for more details.

For commercial use, please contact the author, EdgeOfAssembly, at haxbox2000@gmail.com to arrange a licensing agreement.

Connect

@EdgeOfAssembly | Open an Issue

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Made with passion by @EdgeOfAssembly
2+ million neurons. 3D learning. CUDA-powered.