Specializations and Experience

Machine Learning

Entangled systems can be tremendously complex and benefit greatly from compression. Sam Greydanus '17's Senior Thesis looks to approximate Matrix Product States using neural nets, and then compare this method with the standard compression algorithm, Density Matrix Renormalization Groups.

Gaussian Quantum Information

The use of continuous variable quantum information carriers offers a powerful alternative to the traditional use of discrete qubits. John Caramichael '20 is hoping to find a connection between Gaussian quantum information and the use of the Kalman Filter, which manipulates Gaussian distributions.

Classical Simulations

In some cases, quantum computational techniques are more difficult and more expensive than classical techniques. Steven Karson '20 is looking to evaluate the potential of classical methods for simulating high mass molecules, which is typically done with boson sampling.

Stochastic Processes

Stochastic processes add a layer of richness to models that deal with large amounts of uncertainty. John Caramichael '20 is working on coupling a Kalman Filter algorithm with stochastic elements to model climate phenomena such as the Dansgaard-Oeschger events.

Recent Events

Big congratuations to Sam!

Sam Greydanus sucessfully defended his senior thesis: Approximating Matrix Product States with Machine Learning. First thesis out of the Whitfield group. Great job Sam.

Sam Greydanus '17 Represents the United States in the International Physicists' Tournament

His task was to analyze why a chain will “walk” if a short impulse is applied to a long chain spinning around a horizontal axis.