01. Quantum Computing Fundamentals
Target Audience: High school & college students, beginners in quantum computing
Duration: 1 week (5 days, 3–4 hours per day)
Format: Lectures, hands-on simulations, discussions, and mini-projects
Prerequisites: Basic knowledge of algebra and probability; no prior quantum experience required
Day 1: Introduction to Quantum Computing
• What is Quantum Computing? Overview and historical context
• Classical vs. Quantum Computing: Key differences
• Core Concepts: Superposition, Entanglement, and Quantum Parallelism
• Introduction to Qubits: Bloch sphere representation
• Hands-on Activity:
• Use IBM Quantum Experience or a Python-based quantum simulator (Qiskit) to create and visualize qubits
Day 2: Quantum Gates and Circuits
• Quantum Gates vs. Classical Gates
• Basic Quantum Gates: Pauli-X, Y, Z; Hadamard (H); Phase (S, T); CNOT
• Building a Simple Quantum Circuit
• Quantum Measurement: How quantum states collapse
• Hands-on Activity:
• Implement basic quantum circuits using Qiskit
• Visualize circuit operations using quantum circuit diagrams
Day 3: Quantum Algorithms & Computation
• Quantum Speedup & Complexity: Why quantum computers are powerful
• Key Quantum Algorithms:
• Deutsch-Jozsa Algorithm (Introduction to quantum advantage)
• Grover’s Search Algorithm (Faster search)
• Hands-on Activity:
• Simulate Grover’s Algorithm in Qiskit
• Explore circuit execution on a real quantum processor (IBM Quantum)
Day 4: Quantum Cryptography and Applications
• Quantum Cryptography Basics:
• BB84 Protocol: Secure quantum key distribution
• Shor’s Algorithm: Breaking RSA encryption
• Quantum Applications:
• Optimization (e.g., portfolio optimization in finance)
• Machine learning (quantum-enhanced AI)
• Drug discovery & materials science
• Hands-on Activity:
• Simulate BB84 encryption protocol using Qiskit
Day 5: Future of Quantum Computing & Mini Project
• Current State of Quantum Hardware: IBM, Google, Rigetti, and others
• Quantum Error Correction & Noise: Challenges in building scalable quantum computers
• Future Prospects: Quantum AI, Quantum Internet
• Mini Project:
• Students choose one:
• Create a quantum circuit solving a simple problem
• Simulate a quantum teleportation protocol
• Implement a basic quantum algorithm (e.g., Grover’s search)
• Final Presentations: Showcase project results and discuss key learnings
Course Outcome:
By the end of the course, students will:
Understand quantum computing fundamentals and key principles
Gain hands-on experience with Qiskit and quantum circuit simulations
Learn about quantum algorithms and real-world applications
Complete a small quantum computing project
02. Artificial intelligence (AI) Fundamentals
Target Audience: High school & college students, beginners in AI
Duration: 1 week (5 days, 3–4 hours per day)
Format: Lectures, hands-on coding, group discussions, and project work
Prerequisites: Basic programming knowledge (Python recommended)
Day 1: Introduction to AI and Machine Learning
• What is AI? History, scope, and applications
• Types of AI: Narrow AI vs. General AI
• Introduction to Machine Learning (ML): Supervised, Unsupervised, and Reinforcement Learning
• AI Ethics & Bias: Understanding fairness in AI models
• Hands-on Activity:
• Run a simple AI model in Google Colab (e.g., training a small image classifier using TensorFlow/Keras)
Day 2: Data & AI – The Foundation
• Importance of Data in AI: Data collection, cleaning, and preprocessing
• Types of Data: Structured vs. Unstructured Data
• Feature Engineering & Selection
• Hands-on Activity:
• Use Python (Pandas, NumPy) to process and clean datasets
• Explore a dataset using Matplotlib/Seaborn for visualization
Day 3: Supervised Learning – Training AI Models
• Supervised Learning Basics: Regression vs. Classification
• Common Algorithms:
• Linear Regression
• Decision Trees
• Neural Networks (Intro to Deep Learning)
• Hands-on Activity:
• Train a simple classifier using Scikit-Learn (e.g., recognizing handwritten digits with the MNIST dataset)
Day 4: Neural Networks and Deep Learning
• What are Neural Networks? Basics of perceptrons & multi-layer networks
• Introduction to Deep Learning: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs)
• Hands-on Activity:
• Train a CNN to classify images using TensorFlow/Keras
• Use an interactive AI tool like Google Teachable Machine
Day 5: AI Applications & Mini Project
• AI in Real-World Applications: Healthcare, finance, robotics, creative AI
• Future of AI: Generative AI, Quantum AI
• Mini Project:
• Students choose one of the following:
• Build a basic AI chatbot
• Train a sentiment analysis model on text data
• Develop an image classifier
• Final Presentations: Showcase project results and key learnings
Course Outcome:
By the end of the course, students will:
Understand core AI & ML concepts
Gain hands-on experience with Python, Scikit-Learn, and TensorFlow
Learn to preprocess data and build simple AI models
Complete a small AI project