Session 3 – Intro to Quantum Machine Learning (QML) | QML track | Quantum AI Lab скачать в хорошем качестве

Session 3 – Intro to Quantum Machine Learning (QML) | QML track | Quantum AI Lab

🚀 Quantum Machine Learning Explained | Data Encoding, VQC & Optimization What happens when Artificial Intelligence meets Quantum Computing? In this video, we break down the fundamentals of Quantum Machine Learning (QML) and explore how classical data can be processed on quantum computers using the CQ (Classical → Quantum) approach. If you're interested in Quantum Computing, Machine Learning, AI, Qiskit, or Variational Quantum Circuits, this session is the perfect starting point. 🔹 What You’ll Learn: •⁠ ⁠The quantum workflow vs. classical ML workflow •⁠ ⁠Three main data encoding methods: Basis Encoding Amplitude Encoding Angle Encoding •⁠ ⁠Variational Quantum Circuits (VQC) and the role of the Ansatz •⁠ ⁠Measurement using expectation values ( \langle Z \rangle ) •⁠ ⁠Optimization using the Parameter-Shift Rule and non-gradient optimizers like COBYLA 🔑 Key Concepts: •⁠ ⁠Qubit requirements in different encodings •⁠ ⁠Expectation value to probability conversion •⁠ ⁠How gradients are computed in quantum circuits Track Coordinator: Eng. Abdelrahman Elsayed Quantum Software Engineer Intern Brightskies Director of Education Egypt Quantum Computing Community (EgQCC) MSc Student @Alexandria University Classical Machine Learning Instructor: Eng. Sama Samer AI Engineer | Machine Learning & Data-Driven Solutions Host: Eng. Menna Abd-ElGawad AI/ML Engineer at Fusion Minds AI MSc Student @AAST LinkedIn links: iQafé:   / iqafe   EgQCC:   / egypt-quantum-computing-community   #QuantumComputing #QuantumMachineLearning #QML #AI #MachineLearning #Qiskit #Python #VariationalQuantumCircuits #QuantumPhysics #DataScience