• Quantum Computing Applications
• Introduction to Quantum Mechanics
• Quantum postulates
• Quantum Bits and Qudits
• No Cloning Theorem
• Quantum Gates
• Quantum Measurements
• Bell’s inequality

Learning outcomes:

This module will introduce students to the applications of quantum computing in solving real-world problems. Students will be equipped with key topics, such as the difference between classical and quantum computing, qubits, quantum gates, the no-cloning theorem, quantum measurements, and quantum computing applications.

• Quantum Algorithms Applications
• Introduction to Qiskit
• Shor’s Algorithm
• Grover’s Algorithm
• Deutsch-Jozsa Algorithm Implementation
• Bernstein-Vajirani Algorithm
• Simon’s Algorithm

Learning outcomes:

By the end of this module, students will be familiar with quantum algorithms and the software Qiskit. They will be proficient in using Qiskit and have hands-on experience in implementing key quantum algorithms, including Shor, Deutsch-Jozsa, Bernstein-Vazirani, and Simon’s algorithms. This knowledge will enable students to apply quantum algorithms to solve problems efficiently. Programme offered through the Continuing Education Centre, IIT Roorkee

• Applications for Quantum Communications and Networks
• Foundations of Optical Communication Systems
• Quantum Key Distribution (QKD) protocols
• QKD System Integration and Practical Considerations
• Quantum Teleportation
• Quantum Repeaters
• Quantum Cryptography

Learning outcomes:

By the end of this module, students will have proficient understand of quantum communication protocols, including quantum key distribution protocols, and how can one apply them in transferring data from one entity to another securely. They will also learn about quantum communication and networking enabler applications such as teleportation, repeaters and cryptography.

• Basics: Complex number, Basics of Linear Algebra, and Computational Complexity
• Fundamentals of Machine Learning for Quantum Systems
• Quantum Data Encoding and Feature Maps
• Quantum Support Vector Machines (QSVMs) using PennyLane and Qiskit
• Quantum Neural Networks (QNNs) using PennyLane
• Hybrid Quantum-Classical Architectures

Learning outcomes:

This module will make students develop a rigorous understanding of quantuminformation processing and quantum-enhanced machine learning models suchas QSVMs and QNNs. They will gain proficiency in implementing variationalquantum circuits and applying gradient-based optimization using the parametershift rule. They will learn to construct hybrid quantum-classical models andperform supervised learning tasks using open-source quantum frameworks suchas Qiskit and PennyLane. They will develop an understanding of quantum dataencoding and how to evaluate QML models under realistic noise settings.Programme offered through the Continuing Education Centre, IIT Roorkee

• Implementing QSVM for Binary Classification on a Wine dataset
• Hybrid QNN for Multi-class Classification using Image dataset
• Variational Quantum Eigensolver for Molecular Ground State Estimation
• Solving Max-Cut Problem with Quantum Approximate Optimization Algorithm
• Quantum Generative Adversarial Networks (QGANs)

Learning outcomes:

Students will be able to apply QML models to real-world problems in classification, regression, and optimization. They can build and train their own quantum GANs for discrete data distributions and assess convergence using statistical divergences. They will be proficient in solving simulation problems using VQE and QAOA for combinatorial optimization tasks. They will translate theoretical knowledge into deployable pipelines for applications in domain