add a blurb about algodiff 2024 and gitignore

.gitignore

@@ -9,4 +9,7 @@ Gemfile.lock
 
 # Ingore files created by npm
 node_modules
-package-lock.json
+package-lock.json
+
+# ignore mac fs indexing files
+*.DS_store*

_talks/2024-09-17-talk-1.md

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+---
+title: "Talk on algorithmic differentiation of QR factorization"
+collection: talks
+type: "Talk"
+permalink: /talks/2024-09-17-talk
+venue: "University of Illinois, Chicago"
+date: 2024-09-17
+location: "Chicago, Illinois"
+---
+
+This is a talk at the Algorithmic Differentiation 2024 conference.
+
+I speak about extending the QR factorization gradient for backprop to the wide
+case, software implementations and more.
+
+[Slides](https://rlucas7.github.io/talks/algodiff_2024_slides.pdf)
+
+[Equation handout sheet](https://rlucas7.github.io/talks/algodiff_2024_eqns_handout.pdf)
+
+
+Our section of talks included a wonderful keynote by
+[Nathan Killoran](https://www.artsci.utoronto.ca/news/alum-nathan-killoran-knows-lot-about-quantum-computers-and-he-wants-you-know-about-them-too)  from
+[Xanadu](https://www.xanadu.ai/), a quantum computing firm based in Toronto,
+Canada.
+
+After Nathan worked through the prerequisites of qubits, bras & kets etc.
+he then went on to discuss how the Xanadu toolkit often relies on Tensorflow
+and PyTorch for backend Machine Learning pipelines-A sensible thing to do IMO.
+
+However, one of the limitations he highlighted was that for quantum, the
+operators need to support complex entries. For many algorithmic differentiation
+routines using linear algebra, there are rough edges for the complex values.
+
+It was a nice motivation for why we need complex values inside our matrix
+operators.
+
+In my talk I mostly skipped over the complex values in the
+interest of time. I had focused more on the real valued case because my
+assumptions and experience were on applications uses real values. In hindsight
+any future talks I give on this topic will likely have slightly more emphasis
+on complex values, at least noting that you need them to do quantum computing.
+
+Yet another thing I learned at the conference.
+
+In our talk I tried to focus on how the two equations are mathematically
+equivalent-equations 3.3 and 3.8 of our paper. The fact that they're equivalent
+mathematically means that there is some flexibility on implementation.
+
+However, from an empirical perspective the runtime seems to be better if you
+use equation 3.3. It also works regardless of if you're using real or complex
+values whereas the equation 3.8 requirements an adjustment along the main
+diagonal entries which is further code and maintenance burden. For more details
+take a look at the slides or better yet read our paper.
+
+[Krishna's](https://www.anl.gov/profile/sri-hari-krishna-narayanan) talk followed
+mine. He also gave a great talk. He spoke on
+quantum computing experiments and some rough edges when you have eigenvalue
+problems with multiplicity-either geometric or algebraic. Apparently a way to
+handle this has been known in the algorithmic differentiation community for
+awhile but not all libraries have this corner case implemented or supported.
+Krishna shared the paper with me and now I've got a TODO
+item to read this paper closely-it's pretty dense.
+
+I'll probably write up a listicle with some key points and takeaways at some
+point soon.
+
+In the interim I'll thank the Algorithmic Differentiation 2024 organizers
+for their hard work and to the attendees for fruitful discussions. Last but not
+least, thanks to the Department of Energy (DOE) for offsetting the costs associated
+with the conference so that the attendees can register at a reduced rate.
+
+IMHO the AI revolution wouldn't be happening were it not for the DOEs visionary
+leadership and nurturing of the Algorithmic differentiation community.