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Hello #quantum and #MachineLearning friends. I would like to finally share with you https://quantumalgorithms.org"> https://quantumalgorithms.org an open-source set of lectures notes on quantum algorithms, with a particular focus on machine learning and data analysis on #quantumcomputers.
Why? Because there is a huge gap between research papers and educational grade material on quantum algorithms. And it shouldn’t. I believe there should be a simple (but not easy) way to learn “how to quantum algorithm”.
What guided the development of the website is the desire to have a place where students can find all the math (concentration ineq., linear algebra, error propagations) and toolset (quantum subroutine, computational models, etc..) that one needs to write new #quantumalgorithms.
Who is behind this endeavor? It started as a refactoring of my PhD thesis and my old blog. Now also the friend and colleague @ikiga1 is working on it! As it’s an open source project you can find how to contribute on https://github.com/scinawa/quantumalgorithms.org">https://github.com/scinawa/q...
My current affiliation, which is really supportive and helpful beyond any expectation is @quantumlah and I wrote my thesis at @IRIF_Paris with Iordanis Kerenidis and @f_magniez with the help of Atos Quantum Lab @Atos .
Quantum technologies have the power to profoundly impact our society: people must have a way to understand what’s behind the future quantum version of the algorithms that are already governing our lives.
In this sense this website is dedicated to all #cypherpunks : civil liberties through complex mathematics.
We are proudly supported by @unitaryfund a non profit whose mission is to create a quantum technology ecosystem that helps the most people, in a way that ensures that the benefits of these tools are widely, swiftly, and equitably distributed. Our values are aligned.
There are also 5 students of the Mentorship program of the @qosfoundation that are working hard to add new content to the website. Soon we will add algorithms for Monte Carlo, perceptron, graph theory, lower bounds, dynamic programming, and others.
