Xe - Post-Quantum AI

A lightweight conversational AI framework combining quantum-inspired neural networks with domain knowledge and post-quantum cryptography utilities.

Model Description

Xe is a hybrid AI system featuring:

• Quantum-inspired Neural Network: A feedforward neural network with attention-like mechanisms
• Conversational Brain: Intent classification and learned response generation
• Knowledge Base: Comprehensive domain knowledge in philosophy, education, governance, literature, mathematics, computer science, and physics
• Emotional Intelligence: Empathy-based response adaptation
• Post-Quantum Cryptography: Utilities for quantum-resistant security

Model Architecture

• Type: Quantum-inspired feedforward neural network
• Input Size: 64 features (bag-of-words representation)
• Hidden Layers: 2 layers with 32 and 11 neurons respectively
• Activation: ReLU for hidden layers, linear for output
• Learning Rate: 0.01 (optimized)
• Loss Function: Mean Squared Error

Training Data

The model was trained on:

• 80 conversation samples (expanded training)
• Comprehensive knowledge base across 17 domains
• Philosophical, scientific, mathematical, and cultural knowledge
• Last training: 2026-07-22

Knowledge Domains

The model is expert in:

• Humanities: Philosophy, literature, history, art
• Sciences: Physics, biology, chemistry, astronomy
• Mathematics: Calculus, algebra, statistics, logic
• Social Sciences: Psychology, law, economics, governance
• Technology: Computer science, AI, current science
• Business: Marketing, engineering, medicine

Intended Use

• Primary: Conversational AI and chatbot applications
• Secondary: Knowledge retrieval and educational assistance
• Domains: Philosophy, education, governance, literature, mathematics, computer science, physics

Limitations

• The model is designed for conversational purposes and may not handle all edge cases
• Knowledge is based on training data up to July 2026
• Not suitable for critical decision-making without human oversight

Last updated: July 21, 2026

Usage

from huggingface_hub import hf_hub_download
import json
import numpy as np

# Download model files
model_path = hf_hub_download(repo_id="Travellers/xe", filename="xe_model.json")
brain_path = hf_hub_download(repo_id="Travellers/xe", filename="xe_brain.json")

# Load model
with open(model_path) as f:
    model_config = json.load(f)

# Load brain data
with open(brain_path) as f:
    brain_data = json.load(f)

Installation

pip install xe

CLI Usage

xe chat          # Start interactive chat
xe train         # Train the model
xe optimize      # Run quantum-inspired optimization
xe keygen        # Generate post-quantum keypair

Example Conversation

User: Hello
Bot: Hi there! I'm Xe, your post-quantum AI buddy.

User: How are you?
Bot: Oh, I'm wonderful, actually! Every chat teaches me something.

User: What is the derivative?
Bot: The derivative measures instantaneous rate of change: f'(x) = lim[h->0] (f(x+h)-f(x))/h.

Files

• xe_model.json: Neural network weights and architecture
• xe_brain.json: Conversation history, learned responses, and knowledge
• README.md: This documentation

License

MIT License - See LICENSE file for details.

Author

Created by the Xe AI project. A post-quantum conversational AI framework.