May 4 in Berkeley: Advanced Eigenvalue Algs, Julia Metaprogramming, and Common Lisp for Julians

Text below is from the event page for an event on Sat May 4 in Berkeley CA:

We are happy present a great three-speaker event at Rigetti Computing [1] in Berkeley; at the beginning of the event we will hear how Julia is used in Rigetti’s development of quantum computers. This will be a wonderful event; see details below. Please join us!

Dan Girshovich is a software engineer on the Quantum Computing team at Rigetti; we’ll start with an overview from him of Julia’s metaprogramming facilities, including expressions, macros, and generated functions. This overview will include real-world examples of metaprogramming in the Julia ecosystem, as well as a tips for most effectively leveraging these features.

Robert Smith is Director of Software Engineering at Rigetti; he will speak on “Common Lisp for Julia programmers”, and possible directions for Julia’s future based on a decade of experience writing Lisp. Robert’s abstract: “Common Lisp is an old language by today’s standards. It has its roots in the early eighties, and was formally standardized through ANSI in 1994. Despite its age, it has several high-quality free and commercial implementations, and it has many language features still not commonly found. Some of these language features, like multiple dispatch and macros, have made their way into Julia.”

We’ll then have a talk from our only non-Rigetti speaker; Brendan Gavin is a Machine Learning Research Scientist at [2]; he’ll talk on “Exploring advanced eigenvalue algorithms with Julia”. His abstract: “The traditional workflow for discovering new numerical algorithms consists of doing math on paper, and then implementing that math using a high performance programming language like C++ or Fortran. This workflow requires a researcher to spend a lot of time and attention on software design, which often distracts from the work of algorithm development. Julia makes this process easier by allowing one to implement algorithms in a natural way, without having to sacrifice computational performance. In this talk I’ll discuss how this worked out for me when I used Julia for exploring variations of the FEAST algorithm, which is an advanced technique for solving eigenvalue problems.”