/team_FluxoniumAI_QML

Womanium Quantum+AI 2024 Projects

Primary LanguageJupyter Notebook

QML-for-Conspicuity-Detection-in-Production

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.

Project Information:

Team Size:

  • Maximum team size = 2
  • While individual participation is also welcome, we highly recommend team participation :)

Eligibility:

  • 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 Fraunhofer ITWM. Please review the eligibility criteria for QSL fellowships in the project description below.

Project Description:

  • Click here to view the project description.
  • YouTube recording of the project description - link

Project Submission:

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 Information:

Team Fluxonium_AI
Team Member 1:

  • Full Name: Hoang Anh Nguyen
  • Womanium Program Enrollment ID (see Welcome Email, format- WQ24-xxxxxxxxxxxxxxx): WQ24-CvzDNvSAiGFZB0N

Team Member 2:

  • Full Name: Tu Uyen Tu Le
  • Womanium Program Enrollment ID (see Welcome Email, format- WQ24-xxxxxxxxxxxxxxx):

Project Solution:

We have done 4 subtasks in the projects with reports. In the final one, we used the sub sample data with classical classification, we still have not done yet the quantum classification.

Project Presentation Deck:

Upload/ Link a 3min. presentation deck here.

See project presentation guidelines here