Curriculum

Five main topics will be covered: general engineering principles behind high performance numerical computing (including the use of accelerators like GPUs); state-vector simulation techniques; tensor network theory and practice; fast simulation of Clifford circuits and error correcting codes; discrete event modeling for quantum networks.

Most of the practice will be in the Julia programming language due to the ease with which one can introduce low-level high-performance constructs in it, without losing the dynamic nature and ease of prototyping available in languages like Python. However, practical tips and guides will be provided for programming in Python, Rust, and C/C++, as well.

Multiple hackathons, will be available in the evenings, providing opportunities to work on personal research projects with the help of the instructors, and with bounties available for work on open source projects.

The summer school will end with multiple showcases, workshops, and hackathons from academic and industry partners, demonstrating how the techniques discussed during the summer school are currently applied at the cutting edge of science.

In the extended sessions we will cover many of the following topics:

Confirmed Lecturers

Stefan Krastanov: CS and Physics professor at University of Massachusetts Amherst, lead of the QuantumClifford.jl project and of the NSF Center for Quantum Networks virtual testbed

Katharine Hyatt: Core Julia developer, past scientist at AWS quantum information science division, and past member of the Flatiron institute