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Indian Institute Of Technology Delhi  

Programme Starts
29th June, 2025

Programme Fees
₹1,79,000 + GST

Duration
06 Months

Programme Overview

Unlock the future of technology with IIT Delhi's Certification in Quantum Computing and Machine Learning. This comprehensive programme empowers you to grasp the fundamentals of quantum computing and machine learning while exploring their real-world applications. Led by renowned IIT Delhi faculty and industry experts, you will gain hands-on experience with cutting-edge tools and frameworks, equipping you with the skills to solve complex problems and drive innovation.

Programme Highlights

Comprehensive coverage of quantum computing and quantum machine learning

Taught by renowned IIT Delhi faculty

Live tutorials and lab practice sessions

Doubt clearing sessions

One-day campus immersion

Programme Content

Module 1: Introduction to Quantum Computing

  • 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.

Module 2: Postulates 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.

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Module 3: Introduction to 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. They will be proficient in using Qiskit and have hands-on experience in implementing key quantum algorithms, including Deutsch-Jozsa, Bernstein-Vazirani, and Simon’s algorithms. This knowledge will enable students to apply quantum algorithms to solve problems efficiently and understand their quantum advantage in specific use cases.

Module 4: Quantum Fourier Transform and Related Algorithms

  • Quantum Fourier Transform
  • QFT implementation in Qiskit
  • Quantum Phase Implementation
  • Quantum Phase Estimation in Qiskit
  • Shor’s Period Finding Algorithm
  • Grover’s Search Algorithm

By the end of this module, students will have a comprehensive understanding of the Quantum Fourier Transform and its applications in quantum algorithms. They will be proficient in using Qiskit to implement these algorithms and tackle real-world problems in quantum computing, including cryptography and search tasks.

Module 5: Quantum Machine Learning

  • 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

By the end of this module, students will have a solid grasp of quantum machine learning techniques and their practical implementation. They will be equipped with the skills to use quantum algorithms for data encoding, linear system solving, regression, clustering, dimensionality reduction, and classification, ultimately enhancing their ability to address complex machine learning challenges.

Module 6: Quantum Deep Learning

  • Hybrid Quantum-Classical Neural Networks
  • Classification using Hybrid Quantum-Classification Neural Network
  • Quantum Neural Network for Classification on Near-Term Processors

By the end of this module, students will have a strong understanding of quantum deep learning concepts and practical implementation. They will be able to design, train, and evaluate hybrid quantum-classical neural networks for classification tasks, especially on near-term quantum hardware, enhancing their capabilities in quantum-enhanced machine learning and deep learning.

Module 7: Quantum Variational Optimization and Adiabatic Methods

  • Variational Quantum Eigensolver
  • Expectation Computation
  • Implementation of the VQE Algorithm
  • Quantum Max-Cut Graph Clustering
  • Quantum Adiabatic Theorem
  • Quantum Approximate Optimization Algorithm
  • Quantum Algorithm for Finance

By the end of this module, students will have a comprehensive understanding of quantum variational optimisation techniques and adiabatic methods. They will be able to implement quantum algorithms like VQE, QAOA, and apply them to solve problems in quantum chemistry, graph clustering, optimisation, and finance. This knowledge will empower students to leverage quantum computing for practical problem-solving across various domains.

Tools

  • Qiskit-based programming

Projects

  • 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 Support Vector Machine Learning Algorithm
  • To Solve any Combinatorial Optimisation Problem (Like Knapsack) Using a Quantum Annealing Approach
  • Comparative Study of Data Preparation Methods in Quantum Clustering Algorithms
  • To Calculate the Ground State Energy of a Simple Molecule (H2, LiH, or H2O) Using VQE
  • Variational Quantum Classifier
  • Implementing Grover’s Algorithm and Proving Optimality of Grover’s Search (Bounded Error and Zero Error)
  • To Implement Grover’s Search Algorithm Where 1101 Is the Marked State
  • Quantum Computing for Finance
  • To Solve Crop-Yield Problem using QAOA and VQE, and Run the Same on Real Quantum Computer
  • Analysis of Solving Combinatorial Optimisation Problems on Quantum and Quantum-like Annealers
  • Quantum Convolutional Neural Network for Classical Data Classification
  • Research on Quantum Computing usage to Expedite the Drug Discovery Process (Life Sciences)
  • To Implement Shor's Code in Qiskit with Noise Models
  • To Understand and Implement Quantum Counting
  • Enterprise Intelligence - Managed Services with Quantum Computing
  • On-ground Implementation of Quantum Key Distribution in Indian Navy
  • Implementing MC Simulations using Quantum Algorithm (Financial domain)
  • To Design and Build an Educational Game Using Fundamentals of Quantum Computing
  • Solving Travelling Salesman Problem Using QAOA
  • Implementing Clinical Data Classification by Quantum Machine Learning (QML)
  • To Understand and Implement Quantum
  • Carry-Save Arithmetic
  • To Implement Shor's Algorithm to Factor 49
  • To Understand and Implement Grover Search-Based Algorithm for the List Coloring Problem
  • Optimisation Problem Where We Try to Find the Best Solution to Coal Overburden Problem with Depth and Coal Quantity Mined
  • Implementing HHL Algorithm and Proving BQP-completeness of Matrix Inversion
  • Quantum Convolutional Neural Network-based Medical Image Classification
  • Quantum Convolutional Neural Network
  • Quantum Computing for Finance
  • Differential Detection of Internal Fault of an Electrical Network: A Comparison with Classical vs Quantum Approach
  • Major Area: Implementing any One Quantum Algorithm and Understanding Classical vs Quantum Hardness of Problems
  • Quantum Computing and Information Security
  • Feature Selection in Machine Learning Using Quantum Computing

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CERTIFICATION

  • Candidates who score at least 50% marks overall and have a minimum attendance of 40%, will receive a ‘Certificate of Successful Completion’ from CEP, IIT Delhi.
  • Candidates who score less than 50% marks overall and have a minimum attendance of 40%, will receive a ‘Certificate of Participation’ from CEP, IIT Delhi.
  • The organising department of this programme is the Bharti School of Telecommunication Technology and Management, IIT Delhi.

Note: For more details download brochure.

ELIGIBILITY CRITERIA

  • Educational Background:
    Graduation in any of these disciplines: B.Tech/BE; BCA/MCA; B.Sc in all streams; BA/MA in mathematics.

Class Schedule

Saturdays and Sundays:
8:30 A.M. - 10:00 A.M.

Meet Our Programme Coordinator

Dr. Abhishek Dixit
Associate Professor
Department of Electrical Engineering,
Indian Institute of Technology Delhi

Dr. Abhishek Dixit received his M.Tech. degree in Opto-electronics and Optical Communication from the Indian Institute of Technology (IIT) Delhi in 2010 and his Ph.D. degree in Computer Science Engineering from the Department of Information Technology (INTEC), Ghent University, Belgium, in 2014. Since 2015, he has been an Assistant Professor at IIT Delhi, where he has taught courses related to Optical Communications, Signal Processing, Communications Engineering, and Networking. Recently, he started actively researching the use of Machine Learning to improve the performance of conventional and quantum communications systems.

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He has also taken an NPTEL course on the Principles of Digital Communications. Before joining IIT Delhi in December 2015, he served for a semester (July 2015 – December 2015) as an Assistant Professor at IIT Mandi and as a Post-doctoral Researcher (December 2014 – June 2015) at Ghent University, Belgium. He is leading research activities at IIT Delhi in the area of Optical Communications and Networking. In this context, he has been involved in a large number of Indian projects.

He has also carried out several consultation projects in the area of railway signalling. He has published over 30 international journal articles (IEEE JSAC, IEEE Communications Magazine, Journal of Lightwave Technology, Journal of Optical Communications and Networking, IEEE Networks, IEEE Transactions on Network and Service Management, IEEE Access, IEEE Sensors, IEEE Open Journal of the Communications Society, etc.) and over 50 publications in international conferences.

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Testimonials

ABHIJEET KUMAR
Director, Data Science
Fidelity Investments

This course would benefit anyone who wants to understand fundamentally the quantum space. As a data scientist, I liked the quantum machine learning module where we got exposed to how ML models are trained using quantum techniques/hardware. Most of the topics/algos showing quantum advantage was super interesting to be aware of. One should pursue the course to solve real problems/application using quantum considering the state where quantum computing exists as of now.

DEEPTI VAIDYULA
Principal QA Engineer
Altir

The TimesPro interface for learning is very convenient. I really liked the cloud recordings that are provided. Any issues that we had were sorted promptly. I have taken the QCML-03 course, and the lectures were very detailed. The MCQ's and project work made us put the effort that's needed. I would highly recommend the courses offered via TimesPro + IIT to others.

RAJESH SAHASRABUDDHE
Principal Consultant- NFT marketplace
Rezoomex

Great course content, top class delivery and good service by TimesPro, they were always prompt in responding to queries and also resolved them in time. So overall very happy with the course.

VIJAY KARTHIK
AVPM, JPMC

The course starts from ground zero and builds up to a level where you begin to get a good grasp of what quantum computing is and how it is going to disrupt machine learning. It is conducted by IITD Prof. and has great learning value!

ALEKHA BHATT
Tech Architecture Analyst
Accenture

Enrolling in this course was an enlightening journey that opened my mind to the fascinating realm of quantum computing and its applications to machine learning. The instructor's expertise and passion for the subject were truly inspiring.

SATINDER SINGH
Solutions architect
Violetpath Technologies

Quantum Computing course by IIT Delhi, served by TimesPro, is exceptional! In a short term, I gained a solid understanding of complex concepts. Brilliant instructors, practical insights, and engaging discussions.