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 Member 1:

• Full Name: Betül Gül
• Womanium Program Enrollment ID (see Welcome Email, format- WQ24-xxxxxxxxxxxxxxx): WQ24-zlmRFieHjeFCWok

Team Member 2:

• Full Name: Jessica Omuna Anabor
• Womanium Program Enrollment ID (see Welcome Email, format- WQ24-xxxxxxxxxxxxxxx): WQ24-WStowgJdXd9cc0i

Project Solution:

Step 1: Familiarization with PennyLane and JAX

Initially, you need to familiarize yourself with PennyLane and its integration with JAX. To do this:

Review the documentation and tutorial materials for PennyLane and JAX. Implement and present the PennyLane x JAX tutorial.

Step 2: Implementing Quantum Neural Networks (QNNs)

We will tackle Quantum Neural Networks (QNNs) with different scaling approaches. In this step:

Develop QNN prototypes using PennyLane and JAX. Present the results using standard metrics (accuracy, precision, recall, F1 score) and visualization techniques.

Step 3: Benchmarking Against Classical Methods

Finally, we will compare the performance of hybrid quantum algorithms with classical machine learning and statistical methods:

Benchmark the hybrid quantum algorithms against classical methods. Visualize and compare the performance metrics.

Dataset

We will be using the Detecting Anomalies in Wafer Production dataset as another implementation for Task 3 (QNNs). This unique dataset is crucial as the semiconductor industry is heavily invested in improving wafer production processes, positioning our project at the forefront of advancements in this sector.

Results

Detailed results and visualizations will be available in the results directory. Standard metrics used for evaluation include accuracy, precision, recall, and F1-score.

Project Presentation Deck:

Upload/ Link a 3min. presentation deck here.

https://www.canva.com/design/DAGNFQPk_jg/KP7mG-XLC4L2ta6g9nh4-w/view?utm_content=DAGNFQPk_jg&utm_campaign=designshare&utm_medium=link&utm_source=editor

See project presentation guidelines here