The “Foundations of Quantum Algorithms for AI” course is designed to bridge the gap between quantum computing and artificial intelligence. Participants will delve into the principles of quantum algorithms and their applications within the realm of AI. This interactive, project-based course emphasizes hands-on learning, allowing students to engage with real-world problems and develop innovative solutions that leverage quantum computing capabilities. By the end of the program, learners will be well-equipped to contribute to cutting-edge AI projects that utilize quantum algorithms.
Throughout the course, participants will engage in collaborative projects that culminate in a final presentation, encouraging them to publish their findings in Cademix Magazine. This not only enhances their learning experience but also provides an opportunity to showcase their work to a broader audience. The curriculum is meticulously crafted to ensure that participants gain a comprehensive understanding of quantum machine learning concepts, tools, and techniques, preparing them for advanced roles in the field of AI.
Introduction to Quantum Computing Principles
Overview of Quantum Algorithms
Quantum Superposition and Entanglement
Quantum Gates and Circuits
Quantum Speedup in Machine Learning
Grover’s Algorithm for Search Problems
Quantum Support Vector Machines
Quantum Neural Networks
Implementing Quantum Algorithms using Qiskit
Final Project: Developing a Quantum Algorithm for an AI Application