IIT Delhi Certification in Quantum Computing & Machine Learning

About Course

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.

Course Content

Introduction to Quantum Computing
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 Bits
Dirac Notation
Single and Multiple Qubit Gates
No Cloning Theorem
Quantum Interference

Postulates of Quantum Computing
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.

Introduction to Quantum Algorithms
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.

Quantum Fourier Transform and Related Algorithms
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.

Quantum Machine Learning
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.

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

Quantum Deep Learning
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.

Quantum Variational Optimization & Adiabatic Methods
By the end of this module, students will have a comprehensive understanding of quantum variational optimization 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, optimization, and finance. This knowledge will empower students to leverage quantum computing for practical problem-solving across various domains.

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
Comparitive 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 Convulational 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 Quantam 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
Implementing Any One Quantum Algorithm and Understanding Classical vs Quantum Hardness of Problems
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

About Course

What Will You Learn?

Course Content

Introduction to Quantum Computing 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 Bits

Dirac Notation

Single and Multiple Qubit Gates

No Cloning Theorem

Quantum Interference

Postulates of Quantum Computing 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.

Quantum State

Quantum Evolution

Quantum Measurement

Bell’s Inequality Test

Density Coding

Quantum Teleportation

BB84 Protocol

Quantum error correction

Qiskit

Deutsch-Jozsa Algorithm Implementation

Bernstein-Vajirani Algorithm

Simon’s Algorithm

Quantum Fourier Transform

QFT implementation in Qiskit

Quantum Phase Implementation

Quantum Phase Estimation in Qiskit

Shor’s Period Finding Algorithm

Grover’s Search Algorithm

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

Hybrid Quantum-Classical Neural Networks

Classification using Hybrid Quantum-Classification Neural Network

Quantum Neural Network for Classification on Near-Term Processors

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

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

Comparitive 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 Convulational 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 Quantam 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

Implementing Any One Quantum Algorithm and Understanding Classical vs Quantum Hardness of Problems

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

Introduction to Quantum Computing
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.

Postulates of Quantum Computing
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.