CEP, IIT Delhi Certification in Quantum Computing & Machine Learning

• Quantum Bits

• Dirac Notation

• Single and Multiple Qubit Gates

• No Cloning Theorem

• Quantum Interference

Students will be equipped with a thorough understanding of the key topics covered in Module 1, enabling them to work with qubits, quantum gates, Dirac notation, and understand the foundational principles of quantum computing.

• Quantum State

• Quantum Evolution

• Quantum Measurement

• Bell’s Inequality Test

• Density Coding

• Quantum Teleportation

• BB84 Protocol

• Quantum error correction

By the end of this module, students will have a solid grasp of the foundational concepts in quantum computing and be able to apply these principles to solve real-world problems and design quantum algorithms.

• Qiskit

• Deutsch-Jozsa Algorithm Implementation

• Bernstein-Vajirani Algorithm

• Simon’s Algorithm

By the end of this module, students will have a solid foundation in quantum algorithms and hands-on experience implementing key quantum algorithms using Qiskit.

• Quantum Fourier Transform

• QFT implementation in Qiskit

• Quantum Phase Implementation

• Quantum Phase Estimation in Qiskit

• Shor’s Period Finding Algorithm

• Grover’s Search Algorithm

Students learn QFT and its applications, with practical Qiskit implementations for cryptography and search tasks.

• Data Encoding

• HHL Algorithm

• HHL Algorithm Implementation

• Quantum Linear Regression

• Quantum Swap Test Subroutine

• Swap Test Implementation

• Quantum Euclidean Distance Calculation

• Quantum K‑Means Clustering

• Quantum Principal Component Analysis

• Quantum Support Vector Machines

• SVM Implementation using Qiskit

Practical quantum ML techniques for encoding, regression, clustering, dimensionality reduction and classification.

• Hybrid Quantum‑Classical Neural Networks

• Classification using Hybrid Quantum‑Classification Neural Network

• Quantum Neural Network for Classification on Near‑Term Processors

Design, train and evaluate hybrid quantum‑classical neural networks for classification tasks on near‑term hardware.

• Variational Quantum Eigensolver

• Expectation Computation

• Implementation of the VQE Algorithm

• Quantum Max‑Cut Graph Clustering

• Quantum Adiabatic Theorem

• Quantum Approximate Optimisation Algorithm

• Quantum Algorithm for Finance

Variational techniques and adiabatic methods applied to chemistry, optimisation and finance problems.

• Certificate Program in Gen AI Fundamentals (CPGAIF)

• Certificate Program in Advanced LLM Development (CPALLMDGAI)

• Certificate Program in Gen AI Tools & Business Applications (CPGAITBA)

Note: These complimentary courses are offered by TimesPro; IIT Delhi is not responsible for them.

• 10 hours of tutorials teaching programming of quantum machine learning algorithms in Python.

Qiskit‑based programming

• Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

• Analysis and Implementation of Quantum Encoding Techniques

• Quantum Convolutional Neural Network for Classical Data Classification

• Prediction of Solar Irradiation using Quantum SVM

• Combinatorial Optimisation (Knapsack) via Quantum Annealing

• Comparative Study of Data Preparation Methods in Quantum Clustering

• Ground State Energy of Small Molecules (H2, LiH, H2O) using VQE

• Variational Quantum Classifier

• Implementing Grover’s Algorithm and proving optimality

• Quantum Computing for Finance; MC Simulations using Quantum Algorithms

• Clinical Data Classification by Quantum Machine Learning (QML)

• Feature Selection in Machine Learning using Quantum Computing

• ... (full project list as in source)

Career support (offered by TimesPro) including personal branding, LinkedIn profile review, resume creation and review, interview preparation (pre/in/post), job search strategy and curated job opportunities. Note: IIT Delhi is not responsible for career support.