• This project develops a hybrid quantum-classical model to classify handwritten digits ’0’ and ’4’ from MNIST. Grayscale pixels are encoded into quantum states, and unsharp measurements extract features while preserving quantum properties. These features are then processed by a classical neural network. We find that state reuse and measurement unsharpness significantly impact accuracy.