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01. Quantum Computing Fundamentals

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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

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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

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