Womanium Quantum+AI 2024 Projects
Please review the participation guidelines here before starting the project.
Do NOT delete/ edit the format of this read.me file.
Include all necessary information only as per the given format.
- Maximum team size = 4
- While individual participation is also welcome, we highly recommend team participation :)
- All nationalities, genders, and age groups are welcome to participate in the projects.
- All team participants must be enrolled in Womanium Quantum+AI 2024.
- Everyone is eligible to participate in this project and win Womanium grants.
- All successful project submissions earn the Womanium Project Certificate.
- Best participants win Womanium QSL fellowships with NNL. Please review the eligibility criteria for QSL fellowships in the project description below.
All information in this section will be considered for project submission and judging.
Ensure your repository is public and submitted by August 9, 2024, 23:59pm US ET.
Ensure your repository does not contain any personal or team tokens/access information to access backends. Ensure your repository does not contain any third-party intellectual property (logos, company names, copied literature, or code). Any resources used must be open source or appropriately referenced.
Team Member 1:
- Full Name: Iizalaarab Elhaimeur
- Womanium Program Enrollment ID (see Welcome Email, format- WQ24-xxxxxxxxxxxxxxx): SVZvEfwG6geXFfb
Team Member 2:
- Full Name:
- Womanium Program Enrollment ID (see Welcome Email, format- WQ24-xxxxxxxxxxxxxxx):
Team Member 3:
- Full Name:
- Womanium Program Enrollment ID (see Welcome Email, format- WQ24-xxxxxxxxxxxxxxx):
Team Member 4:
- Full Name:
- Womanium Program Enrollment ID (see Welcome Email, format- WQ24-xxxxxxxxxxxxxxx):
The included Jupyter notebook presents a proof-of-concept for a hybrid approach to simulating materials capable of selective diffusion via Quantum Sieving for Hydrogen Isotopes. It serves as the initial step in a pipeline that leverages classical methods to streamline subsequent quantum computer simulations. The classical pre-screening demonstrated here aims to efficiently identify promising candidates, reducing the computational load on more resource-intensive quantum simulations. This approach highlights the potential synergy between classical and quantum computing in materials science research.
(Womanium Quantum+AI 2024 Project - Team FLUX.pdf
See project presentation guidelines here