Hybrid Quantum Neural Network for classification of the MNIST Dataset using Classiq
Quantum Neural Networks involve combining classical neural networks with the advantage of quantum information to create more efficient algorithms. The goal of this project is to understand how we can utilize the quantum capabilities to create quantum layers. We will create a hybrid network consisting of both classical and quantum layers to classify the MNIST dataset.
Mentor: Tal Michaeli
Team Members: Ashmit JaiSarita Gupta (Me), Asif Saad, Roman Ledenov
Project Report: Hashnode blog on Project Report by Ashmit JaiSarita Gupta
[🗸] Understanding the basics of Quantum Neural Networks and Classiq.
[🗸] Decide the Quantum Neural Network Architecture. (VQC?)
[🗸] Pre-process the MNIST dataset for compatibility with our quantum algorithm. Convert pixel values to a format suitable for quantum circuits. (Angle Encoding)
[🗸] Develop a quantum encoding scheme to represent MNIST digits using qubits. This step involves mapping classical data to quantum states.
[🗸] Create Quantum Neural Network.
[...] Experimentation: Training & Testing