IIT Delhi Certification in Quantum Computing and Machine Learning

Course Description

Program overview:

Gain knowledge about Quantum Computing and Machine Learning, and how to apply these technologies to solve real-world problems. This programme is tailor-made for anyone who wants to learn about the future of computing, or who is interested in a career in Quantum Computing or Machine Learning. IIT Delhi’s Certification in Quantum Computing and Machine Learning equips professionals with the knowledge and skills necessary to excel in the rapidly evolving technological landscape. Participants will gain a deep understanding of quantum mechanics, quantum algorithms, and quantum hardware, as well as proficiency in machine learning models, data analysis, and neural networks. The curriculum is crafted by leading experts in the field, ensuring that it addresses current industry needs and future trends.

Programme Timelines

Course Fee:  ₹ 1,69, 000 + Taxes

Class Timings: Saturdays and Sundays: 8:30 am - 10:00 am.

Duration:  The IIT Delhi Quantum Computing and Machine Learning programme duration is 6 Months

45 hours of live sessions

10 hours of live tutorials/labs sessions

45 hours self-paced session recordings

6 hours campus immersion

Who Should Attend?

Learners with a proficiency in Mathematics and Programming, strong passion for emerging technologies, and a desire to be at the forefront of innovation can find great value in exploring the synergies between quantum computing and machine learning.

Scientists and academics with an interest in cutting-edge technologies can explore the intersection of quantum computing and machine learning to push the boundaries of their research.

Professionals already working in the field of data science or analytics can expand their expertise by incorporating quantum computing into their toolkit.

Eligibility Criteria

Graduation/Postgraduation in Science, Technology, Engineering or Mathematical Sciences.

Admission Criteria

Selection based on application review

Certificate and Assessment

Evaluation

60% - Module tests/quizzes

30% - Projects

10% - Attendance

6 tests/quizzes will be held after the completion of each module, from 2nd module onwards.

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.

Sample Certificate

*Only e-certificates will be issued by CEP, IIT Delhi for this programme.

How It Works

Step1: Select course of your interest and register.

Step2: Receive Counselling from our Programme Advisors.

Step3: Get your documents verified and give an Interview if applicable.

Step4: Obtain Offer Letter and Give your Acceptance.

Step5: Pay Preliminary Course Fee.

Step6: Complete Onboarding and commence Course.

Key Highlights of Program

Comprehensive coverage of quantum computing and quantum machine learning

Taught by renowned IIT Delhi faculty

Live tutorials and lab practice sessions

One-day campus immersion

Doubt clearing sessions.

Key Learning Outcomes

Learn the principalities and nuances of quantum computing

Understand the differences between conventional computing and quantum computing

Get equipped with various quantum computing algorithms

Build a strong foundation in the applications of Quantum Computing and Machine Learning

Access to the latest industry insights.

Programme Delivery

Live Online Sessions Delivered Direct-to-Device (D2D).

Campus Immersion

1 day for interaction with candidates at IIT Delhi(optional).

Job Roles

Below are the job roles available in this field:

Quantum Software Developer: Responsible for designing, implementing, and optimising quantum algorithms and software applications using quantum computing platforms. They ensure the software leverages quantum mechanics to solve complex problems more efficiently than classical methods.

Quantum Machine Learning Engineer: Develops and integrates machine learning models with quantum computing techniques to enhance computational capabilities. Their role involves experimenting with quantum algorithms to improve data analysis, pattern recognition, and predictive modelling.

Quantum Data Scientist: Applies quantum computing to process and analyse large datasets, discovering insights and patterns not feasible with classical computing. They design quantum experiments and interpret results to inform business decisions and scientific research.

Quantum Consultant: Provides expert advice on the potential applications and benefits of quantum computing to businesses and organisations. They evaluate current systems, recommend quantum solutions, and assist in the implementation and integration of quantum technologies.

Course 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.

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 Qis kit 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 Eigen solver

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 Colouring 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

Dates and Fees

Programme Fee

Note:

All fees should be submitted in the IITD CEP Account only, and the details will be shared post-selection.

Withdrawal & Refund from Programme

Candidates can withdraw within 15 days from the programme start date. A total of 80% of the total fee received will be refunded. However, the applicable tax amount paid will not be refunded on the paid amount.

Candidates withdrawing after 15 days from the start of the programme session will not be eligible for any refund.

If you wish to withdraw from the programme, you must email cepaccounts@admin.iitd.ac.in and icare@timespro.com stating your intent to withdraw. The refund, if applicable, will be processed within 30 working days from the date of receiving the withdrawal request.

Instalment Schedule

** GST @18% will be charged extra in addition to the fee.

Easy EMI Options Available

Note:

Registration Fee of ₹10,000 will be charged for processing the selected applications only, post confirmation email from the institute. The registration fee is also part of the total programme fee.

An offer letter from CEP, IIT Delhi will be released post the successful receipt of the Registration Fee.

Payment of fees should be submitted in the IIT Delhi CEP account only and the receipt will be issued by the IIT Delhi CEP account for your records.

Loan and EMI Options are services offered by TimesPro. IIT Delhi is not responsible for the same.

Testimonials

Abhijeet Kumar

IIT Delhi certification in Quantum Computing & Machine Learning

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.

 Frequently Asked Questions (FAQs)

What is IIT Delhi Certification in Quantum Computing & Machine Learning?

IIT Delhi Certification in Quantum Computing & Machine Learning is all about Exploring Quantum Computing and Machine Learning fundamentals and their practical applications in real-world problem-solving. This IIT Delhi Quantum Computing and Machine Learning programme is designed for those keen on understanding the future of computing and pursuing a career in Quantum Computing or Machine Learning.

What is the duration of this this IIT Delhi Quantum Computing and Machine Learning programme?

The duration of this quantum computing and machine learning IIT Delhi certification is 5 months.

Who is this IIT Delhi QCML course designed for?

The course is designed for professionals who want to stay ahead of the curve in the field of quantum computing and machine learning.

What are the topics covered in this certification programme?

The certification program covers the following topics and more:

Introduction to Quantum Computing

Postulates of Quantum Computing

Introduction to Quantum Algorithms

Quantum Fourier Transform and Related Algorithms

Quantum Variational Optimisation and Adiabatic Methods

What are the career prospects after completing this IIT Delhi Certification in QCML programme?

Pursuing this IIT Delhi Certification in QCML programme opens excellent opportunities.  These days, industries, including software development, artificial intelligence, healthcare, and medicine, can benefit from quantum computing. High yearly compensation, exciting work, and industry recognition are all benefits of a career in quantum computing. You can work with EV manufacturers, charging infrastructure providers, automakers, renewable energy providers, governmental agencies, and consultancy businesses. 

Reference taken from: https://timespro.com/executive-education/iit-delhi-certification-in-quantum-computing-and-machine-learning