LLY-GP is part of the LILY Project and focuses on optimization parameter-based quantum circuits. It enhances the efficiency of quantum algorithms by fine-tuning parameters of quantum gates. GP stands for Generativ Qubit Processing, which assigns each word the state of a multi-qubit system and recognizes words through a quantum machine learning process. This approach leverages gradient-based optimization techniques to improve the performance of quantum circuits.
The primary goal of LLY-GP is to recognize and assign languages, making it a foundational element in the development of language-aware models. As the LILY Project evolves, LLY-GP will become increasingly important, serving as a critical component in more advanced quantum machine learning models.
LLY-GP is available on the LILY QML platform, making it accessible for researchers and developers.
For inquiries or further information, please contact: info@lilyqml.de.
| Role | Name | Links |
|---|---|---|
| Project Lead | Leon Kaiser | ORCID, GitHub |
| Inquiries and Management | Raul Nieli | |
| Supporting Contributors | Eileen Kühn | GitHub, KIT Profile |
| Supporting Contributors | Max Kühn | GitHub |
LLY-GP is a Quantum Machine Learning model that plays a crucial role in the development of future models within the LILY Project. It combines the gate properties of the DML model with a tokenizer, creating a robust foundation for future applications. This model acts as a precursor to the GLLM model, which leverages the properties of GP to function like a Large Language Model (LLM).
By integrating these innovative techniques, LLY-GP significantly enhances the efficiency and performance of language models. With its ability to merge quantum mechanics and machine learning, LLY-GP is set to play a central role in the development of new quantum-based applications and systems.
The model is based on Generativ Qubit Processing (GP), which assigns each word the state of a multi-qubit system. This method enables precise word recognition and processing through quantum-based machine learning techniques.
graph TD;
A[Start] --> B[LLY-GP Model];
B --> C[DML Model Features];
B --> D[Tokenizer];
C --> E[GP Properties];
D --> E;
E --> F[GLLM Model];
F --> G[Large Language Model Functionality];
G --> H[Quantum-based Applications];
H --> I[End];
Optimization of the model is achieved by fine-tuning the parameters of quantum gates. This enhances the efficiency of quantum algorithms and allows for highly accurate results in language recognition.
We welcome and encourage public collaboration on this GitHub project. If you're interested in contributing, there are several ways you can get involved:
If you have questions or suggestions, feel free to reach out to our team at any time. We're eager to hear your thoughts and are open to discussions about potential improvements or new ideas for the project.
Dive into the repository to understand the current state of the project. You'll find detailed documentation and examples that will help you get up to speed quickly. We recommend checking out the following resources:
- README: Provides an overview of the project and guides you on getting started.
- Documentation: Offers detailed information about the project's architecture, modules, and usage.
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