Qelm, Quantum Enhanced Language Model - designed to integrate quantum computing principles with classical neural network architectures. This model leverages qubits and quantum algorithms, such as Grover’s, to enhance natural language processing (NLP) tasks by efficiently encoding information within qubits. Key Features: 1. Quantum-State-Based Encoding: Embeds more data per qubit compared to classical bits, optimizing information density. 2. Selective Retrieval: Extracts limited classical information per qubit, adhering to the Holevo theorem. 3. Quantum Algorithms Integration: Utilizes algorithms like Grover’s for accelerated search and retrieval processes. 4. Classical-Quantum Synergy: Combines standard neural network components (e.g., attention mechanisms, feed-forward layers) with quantum circuits to improve performance and scalability. Benefits: - Reduced Memory Footprint: Achieves a smaller model size by encoding parameters in qubits. - Enhanced Scalability: Facilitates the development of scalable AI models through efficient quantum encoding. - Quantum Speedups: Implements quantum algorithms to potentially accelerate specific computational tasks within the language model. Technical Overview: - Architecture: The model consists of tokenization and embedding layers that map tokens to vectors, which are then encoded into qubits. Quantum circuits apply transformations and measurements, feeding results into classical neural network layers. - Parameter Efficiency: Uses a quantum parameter store to manage gate angles, reducing memory usage through parameter sharing. - Compliance with Quantum Mechanics: Ensures adherence to the Holevo theorem by limiting classical information extraction to one bit per qubit per measurement. Documentation and Resources: Detailed documentation is available [here](http://qelm.rdbiotech.org/wp-con...) outlining the architecture, mathematical foundations, and implementation specifics. We encourage developers and researchers to review the documentation to understand the integration of quantum computing with classical NLP techniques. Discussion and Contributions: In order to better understand this field and help leverage it for others to learn and better utilize, discussions and contributions are an essential part. If you are a part of wanting to advance this field or understand it better, I encourage you to contribute and ask as many questions as you can. This is an open, friendly community where everyone is welcome. 🔗 [GitHub Repository](https://github.com/R-D-BioTech-A...) | 📄 [Documentation](http://qelm.rdbiotech.org/wp-con...)

Qelm - Quantum-driven language models for next-gen AI

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