Neuromorphic Quantum Computing

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

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

Requires NVIDIA GPU (CUDA) --- CPU fallback in development Full 3D volumetric learning with synaptic plasticity

Live Demo: 2+ Million Neuron 3D Brain

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

---

[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

For smaller test:

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

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

Contributing

See CONTRIBUTING.md

Good first issues:

• Add 3D spike visualization (brain3d.py)
• CPU-only mode (no CUDA)
• 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 [email protected] to arrange a licensing agreement.

Connect

@EdgeOfAssembly | Open an Issue

---

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