Quantum Computation + Quantum Information Research
The overarching objective of our group is to understand the abilities and limitations of new and existing computers to perform physical simulations.
In particular, we are interested in the role that quantum mechanics plays in computation both in terms of quantum computers and classical models of quantum information. Important applications to be considered include the study of compression techniques for quantum systems (e.g. matrix product states, density functional and density matrix theories) and addressing the longstanding question of what (if anything) today's quantum computation can tell us about fermionic systems of applied interest. A wide scope of techniques from condensed matter, to statistical physics, and electronic structure allow for applied and theoretical interests of group members to be accommodated and nurtured.Our Members
Specializations + Experience
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.
In some cases, quantum computational techniques are more difficult and more expensive than classical techniques.
Stochastic processes add a layer of richness to models that deal with large amounts of uncertainty.