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LLY-DML 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. DML stands for Differentiable Machine Learning, emphasizing the use of gradient-based optimization techniques to improve the performance of quantum circuits.
LLY-DML is available on the LILY QML platform, making it accessible for researchers and developers.
For inquiries or further information, please contact: info@lilyqml.de.
This wiki is structured as follows:
| 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 |
| Contributor | Role | Contribution |
|---|---|---|
| Clausia | Support in Development | General development support |
| MrGilli | Support in Quplexity DML Version | Quplexity DML Development |
| Supercabb | Support in Code Development | Codebase contributions |
| Userlenn | Support in Code Development | Codebase contributions |