Winter School on Quantum Computing

Sponsored by TCS Research


** Selected students for Winter Schools 2022 have been informed individually by email about the next steps. **

Dates: 3 to 15 January 2022

Academic coordinator:

Hosted by Indian Institute of Technology Madras

Platform: To be announced. Registered students will be provided the link.

Overview of the School:
The school will provide an introduction on Quantum Computing to students at the level of undergraduate, postgraduate levels. It will also be useful to fresh PhD students who are about to initiate their research. In the 2 weeks we will cover the fundamentals of quantum information and quantum computing and also give an introduction to quantum circuits and gates. On the application front we wish to cover different Communication Protocols, Quantum Algorithms and Quantum Machine Learning. We will also provide hands on experience on using IBM QISKIT for programming quantum computer.

List of subtopics:

  • Overview of mathematical foundations (Linear algebra, Complex analysis, Probability theory)
  • Introduction to Quantum Information (quantum states POVM, Entanglement, Witnessing and measurement of entanglement)
  • Basics of Quantum Computation (Quantum circuits and quantum gates)
  • Communication Protocols (Quantum teleportation, Super dense coding, Quantum Secret Sharing)
  • Quantum Algorithms (Deutsch Josza Algorithm, Quantum Fourier Transform, Grovers algorithm, Variational algorithm and HHLE algorithm)
  • IBM Quantum Composer and Quantum Lab using Qiskit
    • Molecular simulation
    • Quantum Machine Learning and Optimization

Proposed list of speakers (with affiliations):

  • Sibasish Ghosh (IMSc)
  • Anil Prabhakar (IIT Madras)
  • Prabha Mandayam (IIT Madras)
  • S. Aravinda (IIT Tirupati)
  • R. Chandrashekar (IIT Madras)
  • Krishna Kumar (Xanadu Technologies)
  • Peter Rohde (University of Technology Sydney)
  • Shampa Sarkar (TCS Research)
  • Girish Chandra (TCS Research)
  • Nirmal M.R (TCS Research)
  • Sayantan Pramanik (TCS Research)

Background/prior courses recommended:

  • Basic Introduction to Linear Algebra and Probability Theory
  • Introduction to Python