Teach Me Qiskit Award

Awarded for:

Best interactive self-paced tutorial (single or multiple Jupyter Notebooks) that explains a specific focus topic in quantum computing using Qiskit and the IBM Q Experience, evaluated on the selection criteria set forth below.

Submission Requirements

  • Include a short description of the tutorial and its contents during the submission process.
  • Submitter agrees to license the Submission under the Apache License, Version 2.0. Any submitted code may only include libraries that are licensed under or that submitter can license under the Apache License, Version 2.0.
  • Include Jupyter Notebook(s) and any additional documentation needed for the tutorial. 
  • Do not include any personally identifiable info (about yourself, your teammates and your respective organizations) in your video.Video submissions where submitters appear within the video are permitted as long as they do not otherwise disclose any personally identifiable info.



Judging Criteria

Each submission will be scored in each round based on the following criteria with a minimum score of 0 and a maximum score of 25 points, with the final score being the average of the judges’ scores:

5 Points

01. Use of Qiskit/IBM Q Experience.

5 Points

02. Magnitude of submitted material.

5 Points

03. Structure & documentation.

How easy is it for others to use the Jupyter Notebook? Are there obvious errors?

5 Points

04. Creativity & originality of didactic method.

5 Points

05. Visual appearance and design of Jupyter Notebook(s).


Abraham Asfaw

Global Lead of Quantum Education at IBM Q and Qiskit Developer Advocate

Pauline Ollitrault

Pre-Doctoral Research Scientist

Daniel Egger

Research Scientist

Tanapat Deesuwan

Lecturer, Department of Physics at King Mongkut's University of Technology Thonburi