SFIN is a neural language model architecture that explores quantum-inspired mechanisms for natural language processing. Building upon the foundation of transformer models, SFIN incorporates complex-valued representations and principles from quantum mechanics to model semantic relationships in text in novel ways. This approach conceptualizes language understanding as a quantum-like system where meanings exist in superposition and interfere with each other—offering a mathematical framework to capture the multifaceted nature of human cognition.

• Complex-Valued Representations: Extends transformer embeddings to use both real and imaginary components.
• Quantum-Inspired Attention: Adapts attention mechanisms to incorporate quantum interference principles.
• Multi-Scale Processing: Simultaneously processes text at word, phrase, and sentence levels.
• External Memory Enhancement: Integrates memory mechanisms inspired by Neural Turing Machines.
• Quantum-Inspired Evaluation Metrics: Uses concepts like von Neumann entropy for model analysis.
• Explainable Attention Visualization: Provides tools for understanding the model's attention patterns.

SFIN integrates several innovative components:

1. ComplexEmbedding: Extends conventional embeddings to the complex-valued space.
2. PositionalEncoding: Adapts positional embeddings for complex-valued representations.
3. EntangledInterferenceLayer: Reimagines attention with quantum-inspired interference.
4. HierarchicalInterferenceModule: Implements multi-scale processing inspired by hierarchical transformers.
5. AdvancedWaveFunctionCollapse: Novel output layer inspired by quantum measurement.
6. MemoryModule: External memory system building on Memory Networks and Neural Turing Machines.
7. CrossModalFusion: Optional component for integrating multi-modal inputs.

SFIN uses standard language modeling procedures with quantum-inspired enhancements:

• Training Loop: Incorporates curriculum learning for collapse mechanisms.
• Mixed-Precision Training: Utilizes PyTorch amp for efficient training.
• Visualization: Integrated TensorBoard support for monitoring progress.
• Hyperparameter Optimization: Uses Optuna to fine-tune model parameters.
• Evaluation Metrics: Combines classical and quantum-inspired measures (e.g., von Neumann entropy).

• Python 3.8+
• PyTorch 1.10+
• Transformers
• Datasets
• Optuna
• TensorBoard
• tqdm
• matplotlib
• sklearn
• scipy

1. Clone the repository:

   git clone https://github.com/waefrebeorn/SFIN.git
   cd SFIN

2. Install dependencies:

   pip install -r requirements.txt

3. Train a model:

   python sfin.py --mode train --model_dim 768 --epochs 3

4. Evaluate a trained model:

   python sfin.py --mode evaluate --checkpoint checkpoints/sfin_best_model.pt

5. Generate text:

   python sfin.py --mode generate --checkpoint checkpoints/sfin_final_model.pt

6. Explore model internals:

   python sfin.py --mode explain --checkpoint checkpoints/sfin_final_model.pt

• --mode: Operation mode (train, evaluate, generate, explain)
• --hyperopt: Enable hyperparameter optimization
• --checkpoint: Path to model checkpoint
• --batch_size: Override automatic batch size selection
• --learning_rate: Set learning rate (default: 6e-5)
• --epochs: Number of training epochs (default: 15 for quantum settings)
• --model_dim: Model hidden dimension size
• --seed: Random seed for reproducibility
• --enable_memory: Enable memory operations during training (disabled by default)
• --gradient_accumulation_steps: Steps to accumulate gradients (useful for limited GPU memory)

SFIN includes a suite of visualization tools for model interpretability:

• Attention Heatmaps: Visualize attention patterns for each layer and head.
• Token Importance Maps: Assess token contributions during text generation.
• Gradient Flow Analysis: Monitor gradient propagation through the network.
• Quantum Entropy Metrics: Track entropy-based measurements.
• Entanglement Visualization: Display the strength and nature of interference effects.

Visualizations are saved to the visualizations/ directory during training and evaluation.

SFIN builds upon and extends several important research threads:

1. Transformer Architecture: Based on the principles introduced by Vaswani et al. (2017).
2. Quantum NLP: Inspired by quantum natural language processing work (e.g., Coecke, Sadrzadeh & Clark, 2010; Meichanetzidis et al., 2020).
3. Complex-Valued Neural Networks: Extends ideas from Hirose (2012) and Trabelsi et al. (2018).
4. Memory-Augmented Networks: Incorporates elements from Memory Networks (Weston et al., 2015) and Neural Turing Machines (Graves et al., 2014).
5. Quantum Cognition: Grounded in theoretical models by Busemeyer & Bruza (2012) and Pothos & Busemeyer (2013).

Potential extensions and improvements include:

• Pre-training on larger and more diverse corpora.
• Adapting SFIN for additional NLP tasks beyond language modeling.
• Experimenting with alternative quantum-inspired collapse mechanisms.
• Integration with quantum computing frameworks.
• Expanding capabilities for cross-lingual and multi-modal applications.

If you use SFIN in your research, please cite this implementation along with the foundational works:

@software{sfin2025,
  author = {WaefreBeorn},
  title = {SFIN: Semantic Field Interference Network},
  year = {2025},
  url = {https://github.com/waefrebeorn/SFIN}
}

Additional references:

• Busemeyer, J. R., & Bruza, P. D. (2012). Quantum Models of Cognition and Decision. Cambridge University Press.
• Coecke, B., Sadrzadeh, M., & Clark, S. (2010). Mathematical foundations for a compositional distributional model of meaning.
• Meichanetzidis, K., Toumi, A., de Felice, G., & Coecke, B. (2020). Grammar-aware sentence classification on quantum computers.
• Hirose, A. (2012). Complex-Valued Neural Networks: Advances and Applications. John Wiley & Sons.
• Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines.
• Weston, J., Chopra, S., & Bordes, A. (2015). Memory Networks.

This project is licensed under the MIT License.

We extend our sincere thanks to the researchers and practitioners whose foundational work in quantum cognition, complex-valued neural networks, and memory-augmented architectures has greatly influenced SFIN. Special thanks to:

• Quantum Cognition Pioneers: Busemeyer & Bruza for their groundbreaking theories.
• Quantum NLP Innovators: Coecke, Sadrzadeh & Clark; Meichanetzidis et al. for laying the groundwork in quantum-inspired language processing.
• Complex-Valued Network Researchers: Hirose and colleagues for advancing complex neural network methodologies.
• Memory-Augmentation Visionaries: Graves et al. and Weston et al. for their contributions to memory-based architectures.

By integrating a diverse set of inspirations and cutting-edge techniques, SFIN aims to push the boundaries of natural language understanding. We hope this project serves as both a tool and a foundation for further research and development in quantum-inspired NLP.