Hi! I’m Louis Schatzki, a 5th year PhD student in the Chitambar group. My research focuses on quantum learning theory, the goal essentially being learning properties of unknown states. I am also interested in quantum techniques for machine learning/optimization. Outside of quantum, I am very interested in mathematical techniques in ML. Here are some selected topics I have worked on:
- Learning with restricted measurements We have known techniques for learning states and properties of them. But what if we cannot perform highly complicated measurements and are restricted to nearer-term and possibly noisy operations? Some of my recent work has examined learning with noisy measurements ( Arunachalam, Havlicek, Schatzki NeurIPS ’23) and distributed property estimation (Arunachalam, Schatzki STACS 2025).
- Symmetry in quantum information Symmetry plays a fundamental role in physics and representation theory is prominent in quantum information theory. Some of my work has incorporated symmetric priors into quantum machine learning models, leading to provable convergence and generalization bounds (see 1, 2, 3). I have also worked on classical and quantum algorithms for representation-theoretic quantities (Bravyi, Gosset, Havlicek, Schatzki) and implemented these in a publicly available repository–ChaMPS.
- Entanglement theory Entanglement is one of the strangest yet most fundamental aspects of quantum mechanics. A basic yet quite complex task is how to quantify/classify entanglement, a topic I have worked on–Schatzki et. al PRR and Schatzki et. al PRA
Before grad school I worked on a variety of topics, including biophysics (I hold patents on certain microfluidics for example) as well as computational physics.
You can find all of my research here: Google Scholar
In my free time I enjoy rock climbing, reading, and Chado.