QuCNN

Automated vehicle quality assessment using hybrid quantum convolutional neural network.

This project helps to -

• Automate the quality assessment for cracks and scratches of vehicle parts produced by the metal-forming process.
• Demonstrate Quantum Machine Learning model capabilities to automatically assess the quality of vehicle parts.
• Possibly demonstrate the advantage of Quantum Machine Learning over Classical Machine Learning methods.

Audience: - Vehicle Manufacturing Companies like BMW, Audi, Tesla, Nissan, Toyota, etc.

How to use -

1. First we have to upload an image. Click on the choose file button and select an image.

2. The image is sent to the server for processing and returns the result as positive or negative. Positive means the image contains a crack and negative means the image does not contain a tag.

The technologies used in this project are Qiskit, Flask, PyTorch, HTML, CSS.

With the help of this project, we're able to create an automated quality assessment for vehicle manufacturing companies while simultaneously demonstrating the capabilities of Quantum Machine Learning models.

Setup & Installtion

Make sure you have the latest version of Python installed.

git clone <repo-url>

Running The App

python app.py

Viewing The App

Go to http://127.0.0.1:5000