Course Objective
Equip learners with a solid understanding of Quantum Computing and its intersection with Artificial Intelligence, leading to practical implementation of Quantum Machine Learning (QML) models and real-world applications.
Target Audience
- Intermediate to advanced learners in AI and Machine Learning
- Quantum computing enthusiasts
- Researchers and developers interested in future-tech
- Final-year students and professionals looking to specialize in Quantum AI
Course Structure
Module 1: Introduction to Quantum AI
Understand the fusion of Quantum Computing and Artificial Intelligence. This module introduces Quantum AI, compares it with classical AI, and explores its growing relevance in the real world.
Module 2: Basics of Quantum Computing
Build a solid foundation in quantum mechanics concepts such as qubits, superposition, entanglement, and quantum circuits. Learn how quantum computing differs from classical computing in power and complexity.
- Qubits, Superposition & Entanglement
- Quantum Gates and Circuits
- Measurement and Quantum States
- Quantum Speedup and Complexity
Module 3: Quantum Computing Tools
Explore the leading frameworks and platforms used to build and simulate quantum algorithms. Gain hands-on experience with Qiskit, PennyLane, Cirq, and cloud-based quantum simulators.
- Qiskit (IBM)
- PennyLane (Xanadu)
- Cirq (Google)
- Quantum Simulators and Hardware Access (IBM Quantum, Amazon Braket, etc.)
Module 4: Foundations of Quantum Machine Learning
Dive into the core principles of Quantum Machine Learning. Learn how quantum data is represented, how quantum linear algebra works, and how algorithms like Variational Quantum Circuits enable machine learning in the quantum realm.
- Quantum Data Representation
- Quantum Linear Algebra
- Quantum-enhanced algorithms
- Variational Quantum Circuits (VQCs)
Module 5: Quantum AI Algorithms
Discover cutting-edge algorithms at the intersection of AI and quantum computing. Study how models like Quantum k-NN, Quantum SVMs, and Quantum Neural Networks are designed and how they outperform or complement classical models.
- Quantum k-Nearest Neighbors
- Quantum Support Vector Machines
- Quantum Neural Networks
- Quantum Boltzmann Machines
- Hybrid Quantum-Classical Algorithms
Module 6: Hands-on Projects
Apply your knowledge in real coding environments. Follow guided projects using Qiskit or PennyLane to build quantum classifiers, hybrid models, Quantum GANs, and basic quantum reinforcement learning simulations.
With step-by-step code, using Qiskit or PennyLane
- Quantum Classifier using VQC
- Hybrid Model: Classical Preprocessing + Quantum Classifier
- Quantum GAN (QGAN)
- Quantum Reinforcement Learning Mini-Simulation
Module 7: Use Cases & Applications
Explore how Quantum AI is being applied across industries—from drug discovery and finance to natural language processing and climate modeling. Understand both current applications and emerging trends.
- Quantum AI in Drug Discovery
- Quantum Optimization in Finance
- Quantum-enhanced NLP
- Climate Modeling & Logistics
Module 8: Challenges, Ethics & Future Directions
Uncover the limitations and philosophical questions around Quantum AI. Delve into challenges like decoherence, ethical dilemmas, and the role of Quantum AI in the future of Artificial General Intelligence (AGI).
- Noise & Decoherence
- Scalability issues
- Ethical concerns with Quantum AI
- The future of AGI and Quantum computing
Capstone Project
Put everything together in a final project that addresses a real-world problem using Quantum AI. Propose, build, and showcase a hybrid model under mentor guidance—and earn your certification.
Objective: Build a hybrid Quantum AI model addressing a real-world problem
- Project proposal submission
- Mentored development
- Showcase + Certification
Bonus Content
- Interview with a Quantum AI researcher
- Cheatsheets for Qiskit and PennyLane
- Career guide in Quantum AI
Certification
Earn your Quantum AI Specialist Certificate upon completion of all modules and the capstone project.