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 = 2
- 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 Fraunhofer ITWM. 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: Anna Harutyunyan
- Womanium Program Enrollment ID: WQ24-8A99o24hZGcxiqj
Team Member 2:
- Full Name: Sayed Parsa Gharavi Neisiani
- Womanium Program Enrollment ID: WQ24-xPb2c7EEKz94kfa
Project Name: QARS, Quantum Advanced & Relative Solutions
This project aims to enhance conspicuity detection in production, enabling the early identification of improvement measures for individual work steps or sub-processes, thus optimizing the production process. By analyzing process data such as image data or time series, the project seeks to uncover deviations and weak points. Given the time-consuming nature of classical data analysis methods, the project explores the potential of hybrid quantum computing to accelerate this process. The focus is on implementing hybrid quantum algorithms and benchmarking them against classical approaches, including machine learning and statistical methods.
Files Included:
- Task1_Intro.md - Introduction to the project and initial setup.
- Task2_VQA.ipynb - Variational Quantum Algorithms implementation.
- Task3_MNIST.ipynb - MNIST dataset analysis using Quantum Machine Learning.
- Task4_SineFunction.ipynb - Analysis using sine function data.
Task5_the_dragon_level.ipynb- Advanced task implementation and analysis.
See project presentation guidelines here