Quantum Computing – Cohort 2

Last Day for Registration : June 20, 2024

SKU: IIT Madras | Start Date: July 1, 2024 Categories: ,

Course Description

Course – Teaser Video

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Why this Course?

We are witness to a fundamental scientific and technological revolution that uses the unique laws of quantum mechanics — characterised by superposition and entanglement — to perform computing tasks that would be impossible, or at least very hard, even for our supercomputers of today. Quantum computing has taken giant strides over the past decade, with companies like IBM, Google, IonQ and Quantinuum showcasing near-term quantum devices with hundreds of qubits. India has witnessed a spurt of related activity over the last few years, backed by government initiatives such as the National Mission on Quantum Technologies and Applications (NMQTA) and the Quantum-enabled Science and Technologies (QuEST) program.

IIT Madras has active quantum research groups spread across the physics, electrical engineering, computer science, chemistry, biotech and management studies departments. We are also the first institute in the country to join the IBM Quantum Network as a partner and obtain preferential access to their current generation of quantum processors.

Market demands

Today, there is a widespread interest in quantum computing and information, both in academia and increasingly in industry. There is indeed a pressing need for a quantum-skilled workforce across academia and industry in India. Through this NPTEL CODE program we hope to introduce the fundamentals of quantum computing to a wider group of professionals and equip them with the necessary tools required to become an active part of the emerging quantum ecosystem.

What will courses 1 & 2 achieve?

The NPTEL Code program is structured as two 12-week courses, offered in sequence.

The first course, “Introduction to Quantum Computing and Qiskit” starts off by introducing the idea of a quantum bit and its properties and proceeds all the way to teaching and demonstrating basic quantum algorithms like search and factoring. This course is tailored in such a way as to be accessible to anyone with a basic knowledge of linear algebra and python. All the algorithms discussed here will be accompanied by demonstrations on current quantum hardware using IBM Qiskit.

The second course, “Advanced Quantum Computing and Applications” ventures into some of the key quantum algorithms that are being used in today’s era of noisy intermediate-scale quantum (NISQ) devices. This includes the variational quantum eigensolver (VQE), quantum-inspired techniques for solving optimisation problems and quantum machine learning. There is also a module on quantum error correction and quantum error mitigation which are crucial for the success of quantum algorithms in the NISQ era. Finally, this course also introduces the learner to emerging software tools such as qiskit runtime and transpiling quantum circuits.

Mode: This program is completely ONLINE and  consists of 2 courses.

Course 1 : Comprises of 39 hours of recorded videos and 12 online live interactive sessions with the faculty.

Course 2: Comprises of 33 hours of recorded videos and 11 online live interactive sessions with the faculty.

The course is structured in the following manner:

  1. Participants need to watch 2.5-3.5 hours of video content per week, for 12 weeks, leading to a total of about 36 hours of content.

      2. A live interaction session would be scheduled every Saturday with the corresponding faculty to discuss the module content of that week to clarify queries. The duration of this session would be 1-1.5 hours.

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Click on the tab “Module Description” and choose the appropriate module from the dropdown menu to view more information about each modules.

Certification of completion would be awarded to the participants on fulfilling the following criteria

1. Attend all the Live sessions with active participation.

2. Submit assignments and final quiz of both the courses

3. Submit feedback forms for all modules

Course start date: July 1, 2024 

Time: Recorded videos will be released on weekdays and Online interactive sessions on Saturdays.

Last date of Registration: June 20, 2024 

Instructors

COURSE 1:

Dr. Prabha Mandayam, IIT Madras

Dr. Chandrashekar Radhakrishnan, IIT Madras

Mr. Dhiraj Madan (IBM Research, India)

COURSE 2:

Dr. Anupama Ray (IBM Research, India)

Dr. Yamijala Chaithanya Sharma (IIT Madras)

Dr. Prabha Mandayam, IIT Madras

Dr. Ritajit Majumdar (IBM Research, India)

Dr. Dhinakaran Vinayagamurthy (IBM Research, India)

Dr. Chandrashekar Radhakrishnan, IIT Madras

Dr. Kalyan (IBM Research, India)

Dr. Anil Prabhakar (IIT Madras)

Module Description - Course 1

MODULE – 1 : Math Prelims (Linear Vectors Spaces)

Module Duration:

3 hours of recorded videos and 4 live online interactive sessions.

Faculty:

Prof. Prabha Mandayam, IIT Madras

Topics covered:

A brief review of the concepts from linear algebra that will be used in the course. This includes the concept of inner product, orthogonal states, eigenstates, eigenvalues and basis vectors.This module will also introduce the bra-ket notation that will be flowed throughout the course.

Learning Outcomes:

Evaluate inner products between two vectors, identify orthonormal bases, evaluate eigenstates and eigenvalues, ability to work with bra-ket notation.

Applications of the Module

Introduce the basic math concepts and notation required for understanding quantum bits and quantum computing.

MODULE – 2 : Quantum Mechanics Fundamentals (States, Operators and Measurement)

Module Duration :

3 hours of recorded video and 4 live online interactive sessions.

Faculty:

Prof. Prabha Mandayam, IIT Madras

Topics covered:

A quick introduction to quantum theory via two-level quantum systems. This module will discuss the postulates of quantum mechanics and describe quantum states, operators (unitary and Hermitian) and discuss the concept of measurement in quantum theory.

Learning Outcomes:

Superposition Principle, action of Hermitian operators and unitary operators, Z-basis and X-basis measurements

Applications of the Module:

Gain comfort with the basic concepts of quantum theory required for quantum computing.

MODULE 3 : Intro to qubits: Bloch sphere, basic single-qubit gates

Module Duration:

3 hours of recorded videos and 5 live online interactive sessions.

Faculty:

Prof. Prabha Mandayam, IIT Madras

Topics covered:

This module will introduce the idea of a quantum bit or qubit, geometric visualization of the qubit via the Bloch sphere and also discuss some of the logical operations on single qubits (X and Z gates and Hadamard gate )

Learning Outcomes:

Identify states on the Bloch sphere, construct single-qubit quantum circuits and evaluate their outputs.

Applications of the Module :

Design simple quantum circuits

MODULE 4: Two-qubit gates, Bell state circuit, No-cloning theorem and teleportation.

Module Duration:

3 hours of recorded videos and 4 live online interactive sessions.

Faculty:

Prof. Prabha Mandayam, IIT Madras

Topics covered:

This module will introduce multi-qubit systems and the concept of entanglement. It will describe the no-cloning theorem and the quantum teleportation protocol.

Learning Outcomes

Tensor-product notation to describe multi-qubit states, understand why quantum states cannot be copied,understand the difference between product states and entangled states.

Applications of the Module

The concept of entanglement, Bell-state circuits are widely used in quantum computation and quantum communication.

MODULE 5: Physical realizations of qubits, Introduction to IBM Q and Qiskit

Module Duration:

3 hours of recorded videos and 4 live online interactive sessions.

Faculty:

Dr. Chandrashekar Radhakrishnan, IIT Madras

Topics covered:

Discussion on different physical architectures for implementing qubits, with a special focus on superconducting qubits. This module will also introduce the IBMQ platform and the qiskit simulator.

Learning Outcomes: 

Ability to create simple quantum circuits and run them on qiskit simulator as well as quantum hardware.

Applications of the Module: 

Introduction to quantum software and hardware via IBM Q

MODULE 6: Quantum speed-up: Deutsch and DJ algorithm + Demo on Qiskit + Intro to Computational complexity

Module Duration : 

3 hours of recorded videos and 4 live online interactive sessions.

Faculty: 

Prof. Prabha Mandayam, IIT Madras

Dr. Chandrashekar Radhakrishnan, IIT Madras

Topics covered:

Introduce ideas of computational hardness (classical and quantum). Discussion of a simple quantum algorithm to demonstrate quantum speed-up.

Learning Outcomes:

Understand query complexity, implement Deutsch and Deutsch-Jozsa algorithms on qiskit

Applications of the Module :

The techniques learnt in the Deutsch algorithm will be applied in many quantum algorithms.

MODULE 7: Quantum Fourier Transform (QFT) and Phase estimation

Module Duration:

3 hours of recorded videos and 4 live online interactive sessions.

Faculty:

Prof. Prabha Mandayam, IIT Madras

Topics covered:

Discuss the circuit for implementing quantum Fourier transform and show the polynomial scaling. Also discuss the phase estimation algorithm as a simple application of QFT.

Learning Outcomes:

Understand how QFT can be performed using Hadamard and controlled rotation gates; implementation on qiskit.

Applications of the Module:

QFT and phase estimation are the basic steps in the quantum factoring algorithm

MODULE 8: Order Finding and Factoring

Module Duration:

3 hours of recorded videos and 5 live online interactive sessions.

Faculty:

Prof. Prabha Mandayam, IIT Madras

Topics Covered

Discuss the order finding algorithm using phase estimation. Discuss Shor’s factoring algorithm via order finding.

Learning Outcomes

Implement quantum factoring algorithm on qiskit.

Applications of the Module

Integer factorization is used in public key cryptography

Module 9 – Simons, Bernstein-Vazirani + Demo on Qiskit

Module Duration:

3 hours of recorded videos and 4 live online interactive sessions.

Faculty:

Prof. Prabha Mandayam, IIT Madras

Topics Covered

Discuss a few related quantum algorithms such as Simons and Bernstein-Vazirani

Learning Outcomes

Implement Simons and B-V algorithms on qiskit.

Applications of the Module

Quantum algorithms for oracle-based problems, exmaple, hidden string problem.

Module 10 – Grover Search + Demo on Qiskit

Module Duration:

3 hours of recorded videos and 4 live online interactive sessions.

Faculty:

Prof. Prabha Mandayam, IIT Madras

Topics Covered

Explain the quantum search algorithm, the Grover iterate and demonstrate quadratic speed-up.

Learning Outcomes

Implement Grover search on qiskit.

Applications of the Module

Quantum search over an unstructured database.

Module 11 – Amplitude amplification, HHL algorithm + Demo on Qiskit

Module Duration:

3 hours of recorded videos and 4 live online interactive sessions.

Faculty:

Mr. Dhiraj Madan (IBM)

Topics Covered

Explain amplitude amplification and use it to discuss the HHL algorithm for solving a system of linear equations.

Learning Outcomes

Implement HHL on qiskit.

Applications of the Module

Quantum algorithm for solving linear systems of equations.

Module 12 – Public key cryptography + Quantum Key Distribution (BB84)

Module Duration:

3 hours of recorded videos and 4 live online interactive sessions.

Faculty:

Dr. Chandrashekar Radhakrishnan, IIT Madras

Topics Covered

Discuss the concept of (classical) public key cryptography and the RSA protocol. Introduce quantum key distribution (QKD ) and discuss the BB84 protocol.

Learning Outcomes

Understand how quantum concepts like the uncertainty principle lead to unconditional security of publick key cryptography using quantum states.

Applications of the Module

QKD protocols are at the heart of quantum communication networks.

Module Description - Course 2

MODULE – 1 : Quantum Machine Learning I : Introduction

About the Module:

After a brief introduction to classical machine learning, this module will discuss quantum machine learning (QML) techniques such as variational quantum classifier (VQC) and Quantum Support Vector Machines (QSVM)

Faculty:

Dr. Anupama Ray (IBM Research, India)

Learning Outcomes:

Understand and apply QML techniques like VQC, QSVM and quantum clustering.

Applications of the Module

VQC and QSVM are essential QML tools for several data/image classification applications.

MODULE – 2 : Quantum Machine Learning II : Theory

About the Module:

After a brief introduction to classical machine learning, this module will discuss quantum machine learning (QML) techniques such as variational quantum classifier (VQC) and Quantum Support Vector Machines (QSVM).

Faculty:

Dr. Anupama Ray (IBM Research, India)

Learning Outcomes:

Understand and apply QML techniques like VQC, QSVM and quantum clustering.

Applications of the Module:

VQC and QSVM are essential QML tools for several data/image classification applications

MODULE 3 : Quantum Machine Learning III : Applications

About the Module:

After a brief introduction to classical machine learning, this module will discuss quantum machine learning (QML) techniques such as variational quantum classifier (VQC) and Quantum Support Vector Machines (QSVM).

Faculty:

Dr. Anupama Ray (IBM Research, India)

Learning Outcomes:

Understand and apply QML techniques like VQC, QSVM and quantum clustering.

Applications of the Module :

VQC and QSVM are essential QML tools for several data/image classification applications.

MODULE 4 : Quantum Machine Learning IV : Applications

About the Module:

After a brief introduction to classical machine learning, this module will discuss quantum machine learning (QML) techniques such as variational quantum classifier (VQC) and Quantum Support Vector Machines (QSVM).

Faculty:

Dr. Anupama Ray (IBM Research, India)

Learning Outcomes:

Understand and apply QML techniques like VQC, QSVM and quantum clustering.

Applications of the Module :

VQC and QSVM are essential QML tools for several data/image classification applications.

MODULE 5: Variational Quantum Algorithms

About the Module:

This module will discuss the class of variational quantum algorithms, with special focus on the variational quantum eigensolver (VQE).

Faculty:

Dr. Anupama Ray (IBM Research, India)

Learning Outcomes

Understand and implement VQE on qiskit.

Applications of the Module

VQE is widely applied in quantum chemistry.

MODULE 6: Noise and Quantum Error Correction

About the Module:

Describe the theory of noise in quantum devices and introduce ideas of quantum error correction.

Faculty:

Prof. Prabha Mandayam, IIT Madras

Learning Outcomes: 

Understand different noise models like bit-flip, phase-flip, depolarizing and T1/T2 processes. Implement a basic QEC code on IBM Qiskit.

Applications of the Module: 

A basic understanding of noise and QEC is essential for working with today’s noisy quantum hardware.

MODULE 7: Error mitigation for NISQ devices

About the Module:

Introduce error mitigation techniques such as Zero Noise Interpolation and Probabilistic Error Cancellation

Faculty:

Dr. Ritajit Majumdar (IBM Research, India)

Learning Outcomes:

Incorporating error mitigation while programming NISQ hardware like IBM Q.

Applications of the Module:

Quantum error mitigation is widely used to reduce the effects of noise on quantum algorithms in current generation NISQ hardware.

MODULE 8: Qiskit runtime, Transpling, Circuit Cutting

About the Module:

Discuss quantum software techniques such as Qiskit Runtime and Transpling on IBM Q

Faculty:

Dr. Dhinakaran Vinayagamurthy (IBM Research, India)

Learning Outcomes

Ability to transpile quantum circuits on IBM Q and understand the use of qiskit runtime

Applications of the Module

Transpling and Qiskit Runtime are important tools in the IBMQ software stack.

MODULE 9: Quantum communication Protocols

About the Module:

This module will introduce two important quantum communication protocols: quantum teleportation and super-dense coding.

Faculty:

Dr. Chandrashekar Radhakrishnan, IIT Madras

Learning Outcomes

Understand the teleportation circuit and super-dense coding protocol; implement them on IBM Qiskit

Applications of the Module

Teleportation and super-dense coding are two important applications of quantum entanglement. These are widely used in quantum communication theory.

MODULE 10: Introduction to Pulse Programming

About the Module:

Introduce the idea of Pulse Programming with a simple example on IBMQ hardware.

Faculty:

Dr. Chandrashekar Radhakrishnan, IIT Madras

Learning Outcomes

Understand the concept of programming quantum hardware via pulse programming routines.

Applications of the Module

Pulse programming is used to optimize the performance of quantum hardware, and make it more resilient to the effects of noise.

MODULE 11: Solving optimization problems on a quantum computer – Part 1

About the Module:

Introduce techniques such as Quantum Integer Programming (QuIP) to solve optimization problems efficiently on quantum devices.

Faculty:

Dr. Anil Prabhakar (IIT Madras)

Learning Outcomes

Solve quadratic unconstrained binary optimisation (QUBO) problems on IBMQ and quantum annealers like D Wave.

Applications of the Module

QUBOs are widely used for practical problems such as bin-packing, optimal routing including traveling salesman problems.

MODULE 12: Solving optimization problems on a quantum computer – Part 2

About the Module:

Introduce techniques such as Quantum Integer Programming (QuIP) to solve optimization problems efficiently on quantum devices.

Faculty:

Dr. Anil Prabhakar (IIT Madras)

Learning Outcomes

Solve quadratic unconstrained binary optimization (QUBO) problems on IBMQ and quantum annealers like D Wave.

Applications of the Module

QUBOs are widely used for practical problems such as bin-packing, optimal routing including traveling salesman problems.

Eligibility & Fees

Course fees:  

Students -Rs 85,000+ 18%GST

Faculty & Industry – Rs 1,00,000+ 18%GST

 

Options to pay in instalments is available

 

Basic Requirement :

We recommend a background in basic linear algebra and python for the participants

Certification

Certification of completion would be awarded to the participants on fulfilling the following criteria

1. Attending and active participation during all the Live Online Interactive sessions.

2. Submission of Weekly assignments and Module Assessment for all modules.

3. Submit feedback forms for all Modules.

 

Certificate criteria for this course would be as follows:

Total % will be calculated from all 3 categories ( Assignments, Quizzes and Attendance of Live sessions)

Type of certificate that will be issued

75% -100% Successfully completed
50% -74% Completed
25% – 49% Participated
<25% No Certificate

Course Certificate Template

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