The Big Data Approach to Quantum Gravity

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In both Causal Set Quantum Gravity as well as in the String Landscape, we face the challenging tasks of sifting through large state spaces and searching for the set of solutions which best model our physical universe. I demonstrate in this talk how efficient parallel algorithms can give us access to areas of physics previously unstudied due to computational barriers. I first present new methods to accelerate the evolution of causal set Markov chains, which enables us to look for the spontaneous emergence of manifoldlike structure. Then, I use a similar graph theoretic approach to show how a dynamic vacuum selection mechanism naturally emerges in F-Theory and Type IIB String Theory in the context of eternal inflation. We will discuss in detail relevant numerical aspects, including practical limitations and algorithms from graph theory, and how machine learning can be used in future work.