Quantum Machine Learning - CybraneX

Basics of Quantum Computing and Quantum Machine Learning This comprehensive course is designed for beginners and enthusiasts eager to explore the exciting fields of quantum computing and quantum machine learning. Divided into two parts, the curriculum provides a foundational understanding of quantum computing concepts and introduces the basics of quantum machine learning and variational algorithms. ✅Part 1: Basics of Quantum Computing In the first part, students will delve into the fundamental principles of quantum computing, including: ✔️The differences between classical and quantum computing. ✔️ Core concepts such as qubits, superposition, entanglement, and quantum measurement. ✔️ Key quantum algorithms like Grover’s and Shor’s algorithms. ✔️ An overview of quantum gates, circuits, error correction, and hardware. ✔️ Hands-on introduction to quantum programming languages like Qiskit and Cirq. ✔️ Each module is reinforced with quizzes to solidify understanding and includes real-world applications to bridge theory and practice. ✅Part 2: Introduction to Quantum Machine Learning and Variational Algorithms Building on the foundational knowledge from Part 1, this section introduces: ✔️ The principles of quantum machine learning (QML) and its distinction from classical machine learning. ✔️ Variational quantum algorithms, such as the Variational Quantum Eigen Solver (VQE) and Quantum Approximate Optimization Algorithm (QAOA). ✔️ Hybrid quantum-classical algorithms and their practical implementations. ✔️ The course concludes with hands-on examples and case studies, enabling learners to apply their knowledge to implement simple QML algorithms. By the end of this course, participants will gain a solid foundation in quantum computing and machine learning, equipping them with the skills and insights to explore advanced concepts and applications in the quantum domain.