quantum-transformer 0.1.1

A High-Fidelity Quantum-Inspired Transformer for Language Modeling

Quantum Transformer

Quantum Transformer is a specialized framework designed for the development and deployment of quantum-inspired artificial intelligence models. It extends the standard Transformer architecture by integrating quantum gate simulations as a mechanism for attention and feature transformation, providing a novel approach to sequence modeling and representation learning.

Overview

The core objective of the Quantum Transformer project is to provide researchers and developers with a robust toolset for building pre-trained Quantum AI models. By mapping quantum circuit operations (such as rotation gates and entanglement simulations) onto neural network layers, the framework achieves high-fidelity signal processing that mirrors quantum state evolution.

Technical Architecture

The framework is built upon several key technical pillars:

Quantum-Inspired Attention (QIA)

Unlike traditional scaled dot-product attention, QIA utilizes simulated quantum gates to calculate weight distributions. This allows the model to capture complex inter-token dependencies within a high-dimensional quantum-like state space.

Structural Confidence Guard

The framework implements a Shannon Entropy-based monitoring system that evaluates model certainty during autoregressive generation. This provides a mathematically grounded metric for output reliability.

Key-Value (KV) Caching

Optimized inference pathways are implemented using a sliding-window KV cache system, ensuring efficient memory utilization even with absolute positional embeddings.

Installation

The package can be installed via pip:

pip install quantum-transformer

Core Components

• QuantumTransformerLM: The primary language model class, supporting both training and inference.
• BPETokenizer: A professional-grade Byte-Pair Encoding tokenizer for sub-word processing.
• QuantumTransformerConfig: A comprehensive configuration system for defining model hyperparameters and quantum circuit depths.

Usage Example

The following example demonstrates how to load a pre-trained model and perform inference:

from quantum_transformer.model import QuantumTransformerLM

# Load a pre-trained model from the Hugging Face Hub
model = QuantumTransformerLM.from_pretrained("Harishapc01/quantum-qt-4q")

# Execute autoregressive generation
output = model.generate(
    prompt="The integration of quantum mechanics with neural networks",
    max_new_tokens=128,
    temperature=0.7,
    top_p=0.9
)

print(output)

Research and Development

Quantum Transformer is intended for:

• Investigating the scalability of quantum-inspired layers in large-scale language models.
• Developing hybrid classical-quantum architectures for natural language processing.
• Benchmarking the efficiency of quantum gate simulations in high-dimensional feature spaces.

License

This project is licensed under the MIT License.

Contact

Quantum AI Team Lead Developer: Harisha P C Email: [e-mail]