IIT Roorkee Advanced Certificate in Quantum Computing: Algorithms & AI/ML

Module: Quantum Computing

Key Topics: Qubits & Quantum States, Superposition & Entanglement, Quantum Gates & Circuits, Grover’s Algorithm, Qiskit Programming, Quantum Cryptography

Learning Outcomes: Understand quantum principles, implement basic quantum circuits and algorithms, use Qiskit for simulations, and explore applications like quantum cryptography.

Module: Math & Statistics

Key Topics: Descriptive Statistics, Probability Basics, Probability Distributions, Inferential Statistics, Hands-on Statistical Analysis

Learning Outcomes: Apply fundamental statistical concepts; perform descriptive and inferential analyses; interpret results for data-driven decision making.

Module: Python for Data Science

Key Topics: Python Basics, Data Structures, Functions & Modules, NumPy Operations, Pandas DataFrames, Data Visualisation with Matplotlib

Learning Outcomes: Develop Python skills for data manipulation, analysis, and visualisation; handle arrays and structured datasets effectively.

Module: EDA & Data Visualisation

Key Topics: Data Extraction (APIs, Web), SQL CRUD & Joins, Data Cleaning & Wrangling, Exploratory Data Analysis, Correlation & Distribution Analysis, Tableau Dashboards.

Learning Outcomes: Extract, clean, and explore data; analyse patterns and relationships; create interactive visualisations and dashboards.

Module: Machine Learning

Key Topics: Linear & Logistic Regression, Decision Trees, k-Nearest Neighbors, Support Vector Machines, K-means Clustering, ML Evaluation Metrics.

Learning Outcomes: Understand and implement core ML algorithms; evaluate model performance; apply ML to real-world datasets.

Module: AI & Deep Learning

Key Topics: Neural Networks, CNN Fundamentals, Transfer Learning, RNN/LSTM/GRU, NLP & Word Embeddings, Chatbots & Voice AI.

Learning Outcomes: Build deep learning models for structured/unstructured data; implement CNNs, RNNs, and attention-based NLP models; develop AI-driven applications.

• Quantum Computing Applications

• Introduction to Quantum Mechanics

• Quantum Postulates

• Quantum Bits and Qudits

• No Cloning Theorem

• Quantum Gates

• Quantum Measurements

• Bell’s Inequality

This module will introduce students to the applications of quantum computing in solving real-world problems. Students will be equipped with key topics, such as the difference between classical and quantum computing, qubits, quantum gates, the no-cloning theorem, quantum measurements, and quantum computing applications.

• Quantum Algorithms Applications

• Introduction to Qiskit

• Shor’s Algorithm

• Grover’s Algorithm

• Deutsch-Jozsa Algorithm Implementation

• Bernstein-Vazirani Algorithm

• Simon’s Algorithm

By the end of this module, students will be familiar with quantum algorithms and the software Qiskit. They will be proficient in using Qiskit and have hands-on experience in implementing key quantum algorithms, including Shor, Deutsch-Jozsa, Bernstein-Vazirani, and Simon’s algorithms.

• Applications for Quantum Communications and Networks

• Foundations of Optical Communication Systems

• Quantum Key Distribution (QKD) Protocols

• QKD System Integration and Practical Considerations

• Quantum entanglement-based DV-QKD protocols

• Quantum Teleportation

• Quantum Repeaters

• Quantum Cryptography

By the end of this module, students will have proficient understand of quantum communication protocols, including quantum key distribution protocols, and how can one apply them in transferring data from one entity to another securely.

- Basics: Complex Number, Basics of Linear Algebra, and Computational Complexity

• Fundamentals of Machine Learning for Quantum Systems

• Quantum Data Encoding and Feature Maps

• Quantum Support Vector Machines (QSVMs) using PennyLane and Qiskit

• Quantum Neural Networks (QNNs) using PennyLane

• Hybrid Quantum-Classical Architectures

This module will make students develop a rigorous understanding of quantum information processing and quantum-enhanced machine learning models such as QSVMs and QNNs. They will gain proficiency in implementing variational quantum circuits and applying gradient-based optimisation using the parameter shift rule.

• Implementing QSVM for Binary Classification on a Wine Dataset

• Hybrid QNN for Multi-class Classification using Image Dataset

• Variational Quantum Eigensolver for Molecular Ground State Estimation

• Solving Max-Cut Problem with Quantum Approximate Optimisation Algorithm

• Quantum Generative Adversarial Networks (QGANs)

Students will be able to apply QML models to real-world problems in classification, regression, and optimisation and translate theoretical knowledge into deployable pipelines.

As a learner enrolled in this programme, you receive free access to three industry-aligned AI/ML courses — together valued at ₹29,999 — designed to strengthen your applied AI skills and complement your quantum computing journey.

01 | Certificate Program in Advanced LLM Development in Generative AI — Build ChatBots using LangChain, BedRock & Streamlit; Learn LLM model parameters; Fine-tune models via Hugging Face; prompt engineering topics.

02 | Certificate Program in Agentic AI: Unlocking Generative AI Potential — create, evaluate and manage AI agents; hands-on with Auto-GPT, LangChain & CrewAI.

03 | Certificate Program in Gen AI Tools and Business Applications — curated Prompt Library; GenAI across functions; ethical AI and governance.

Disclaimer: The three complimentary Certificate Programmes are offered by TimesPro. IIT Roorkee is not responsible for the same. Certificates for the complimentary programmes will be issued by TimesPro upon successful completion of the Advanced Certificate in Quantum Computing: Algorithms and AI/ML.

• Quantum Computing for Finance

• Implementation of Grover’s Search Algorithm

• Implementation of Shor's Code in Qiskit with Noise Models

• Implementation of Quantum Counting

• Implementation of a quantum random number generator

• Implementation of the CV-QKD Quantum Key Distribution protocol

• Implementation of the SIM-DD Quantum Key Distribution protocol following B92

• Implementation of the teleportation protocol

• Train a QSVM using Qiskit and PennyLane on the Wine dataset and compare kernel methods

• Develop a hybrid quantum-classical neural network and test it on a binary image dataset

• Build and evaluate a quantum GAN for synthetic data generation using real market datasets

• Quantum Convolutional Neural Network for Classical Data Classification

• Implementing Clinical Data Classification by Quantum Machine Learning (QML)

Personal Branding, Business Communication, Career Advancement Strategy, Interview Preparation — includes LinkedIn/profile work, resume creation and review, interview etiquette and mock/interview grooming sessions. Note: Career Support Facility is provided by TimesPro. IIT Roorkee is not responsible for the same.