Programme Content
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.
2. Postulates of Quantum ComputingQuantum 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.
3. Introduction to Quantum AlgorithmsQiskit
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.
4. Quantum Fourier Transform and Related AlgorithmsQuantum 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.
5. Quantum Machine LearningData 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.
6. Quantum Deep LearningHybrid 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.
7. Quantum Variational Optimization and Adiabatic MethodsVariational 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.
8. ToolsQiskit-based programming
9. ProjectsHybrid 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
