Course Name: Introduction to Quantum Computing: Quantum Algorithms and Qiskit

Course abstract

Quantum computing is fast emerging as one the key disruptive technologies of our times. It is a fundamentally new computing paradigm that has the potential to efficiently solve certain challenging problems which cannot be solved efficiently in a classical setting. IBM has made significant investment in this technology and is recognized as a leader in this space. This course will provide introduction to Quantum Computation, starting with basic concepts such as superposition and entanglement, to discussing the quantum circuit model of computation and basic Quantum algorithms that demonstrate the power of computing with quantum bits. We will also introduce the idea of quantum error correction to mitigate the effects of noise in today’s quantum devices. We will have full hands-on sessions for each concept taught using Qiskit, a pythonic way of programming and the IBM Circuit Composer.


Course Instructor

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Prof. Prabha Mandayam

Dr Prabha Mandayam, Assistant Professor, IIT Madras – PhD in Quantum Computing, Caltech.Bio - Prabha Mandayam graduated with a Masters in Physics from IIT Madras and obtained her PhD from the Institute for Quantum Computing at Caltech. After working as a post-doctoral fellow at the Institute for Mathematical Sciences and INSPIRE faculty fellow at the Chennai Mathematical institute, she rejoined her alma mater as faculty in 2014,. Her research interests include quantum error correction and quantum cryptography.
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Prof. Anupama Ray

Dr Anupama Ray, Advisory Research Scientist, IBM Quantum Ambassador and Qiskit Advocate, IBM Research – PhD in Deep Learning, IIT Delhi. Bio- Anupama Ray is an Advisory Research Scientist at IBM Research, India. She is an IBM Quantum Ambassador and a Qiskit Advocate. She completed her Ph.D from Indian Institute of Technology Delhi. With her doctoral research focusing on developing and applying multi-dimensional deep recurrent neural networks for document analysis and computer vision applications. At IBM Research she has been predominantly working in the area of natural language processing: building NLP systems for low-resource languages, domain independent information extraction systems and natural language generation. She has interests in Quantum ML, Quantum NLP, and AI for societal applications. She has published several papers in top tier conferences and journals and has received several Best paper awards. She has been the recipient of the IEEE Best Woman Professional (Early Career) and several awards at IBM Research such as Research Division Award, Eminence and Excellence Award and Outstanding Technical Achievement Award to name a few. She was nominated and selected as a Young Scientist in Global Young Scientist Summit and is an active member of IEEE, AAAI, and Society of Women Engineers (SWE) India.
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Prof. Sheshashayee Raghunathan

Dr Sheshashayee Raghunathan, Senior Engineer IBM Quantum Ambassador and Qiskit Advocate – PhD in Quantum Computing, University of Southern California Bio- Shesha Raghunathan joined IBM in 2011 as part of Electronic Design Automation (EDA) Timing analysis development team. He has worked on various aspect of analysis including noise, timing abstraction and reporting. Currently he has been focusing primarily on EDA 3.0 (analytics in EDA) and is leading timing triage efforts. Since 2018 he has additional responsibility of being an IBM Quantum Ambassador, now a Distinguished Ambassador, and is the team lead for India/SA region. As IBM Quantum Distinguished Ambassador Shesha is amongst few who can officially talk about and for IBM Quantum technology. Shesha got his PhD in Electrical Engineering (Quantum Computing) from University of Southern California, LA in 2010. He has over 10 publications spanning reconfigurable computing, static timing analysis and quantum computing, and has 4 patents to his name.
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Teaching Assistant(s)

No teaching assistant data available for this course yet
 Course Duration : Aug-Sep 2021

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 Syllabus

 Enrollment : 20-May-2021 to 23-Aug-2021

 Exam registration : 17-Jun-2021 to 17-Sep-2021

 Exam Date : 23-Oct-2021

Enrolled

9972

Registered

505

Certificate Eligible

266

Certified Category Count

Gold

8

Silver

72

Elite

110

Successfully completed

76

Participation

64

Success

Elite

Silver

Gold





Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 3 out of 4 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Introduction to Quantum Computing: Quantum Algorithms and Qiskit - Toppers list
Top 1 % of Certified Candidates

HARPREET SINGH WAZIR 95%

NATIONAL INSTITUTE OF TECHNOLOGY SRINAGAR

KONDAPALLY VARUN SRIVATSAV 91%

ELECTRONICS & ICT ACADEMY NATIONAL INSTITUTE OF TECHNOLOGY, WARANGAL

SAHIL SARBADHIKARY 91%

BITS Pilani K K Birla Goa Campus


Top 2 % of Certified Candidates

AKSHIT JOHRY 90%

Oracle

SHIVAM DOSAJH 90%

INDIAN INSTITUTE OF SCIENCE EDUCATION AND RESEARCH (IISER), PUNE

SAYAK BHOWMIK 90%

KOMAL KALRA 90%

SAP Labs India

ARUN KUMAR MAURYA 90%

INDIAN INSTITUTE OF SCIENCE EDUCATION AND RESEARCH (IISER), THIRUANANTHAPURAM


Top 5 % of Certified Candidates

RUPAYAN BHATTACHARJEE 88%

Indian Association for the Cultivation of Science

ISHAN NIMIT MANKODI 87%

INDIAN INSTITUTE OF TECHNOLOGY,MADRAS

ANEKAIT KARIYA 87%

NAFIA SALEEM K P 87%

INDIAN INSTITUTE OF TECHNOLOGY,MADRAS

ARYAMAN MANISH KOLHE 87%

VIT UNIVERSITY-VELLORE

Enrollment Statistics

Total Enrollment: 9972

Registration Statistics

Total Registration : 505

Assignment Statistics




Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.

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Very useful.As it is only introduction to quantum computing it will good if the advanced course is started. So that students may continue in this career at ease