Entangled Quantum Probes

We are among the world’s leaders in developing new technologies for producing entangled neutron beams. We are the first to explore the possibility of using such beams to probe correlations in materials where entanglement of electrons play a role in establishing complex materials properties. We are also developing new quantum dot-based and entangled photon probes. The entanglement of these probes will allow for a millionfold improvement of the mapping of the dark sector at short separations.

Phase Diagram

Topological Quantum Matter

We are integrating advanced mathematical concepts with new quantum probes, such as entangled neutrons, to identify quantum materials with fractional topological excitations. In parallel, we are implementing a fiber-ion quantum machine that uses experimental information to better simulate those topological materials. We also investigate foundational issues, such as detection of fractional excitations (e.g., Majorana fermions) and engineering robust quantum memories.

Precision Measurements and Quantum Certification

We are working to develop quantum sensors, devices that use quantum interference effects to achieve greatly enhanced measurement sensitivity. Our studies towards the development of a fully entangled interferometer would provide unprecedented sensitivity in quantum measurements.

Applications of Quantum Probability Theory to Social and Behavioral Sciences

Classical probability theory is founded on the premise that events are represented as subsets of a larger set called the sample space. Adopting subsets as the description of events entails the strict laws of Boolean logic. Quantum probability theory is founded on the premise that events are represented as subspaces of a vector space. Adopting subspaces as the description of events entails a new logic that relaxes some of the axioms of Boolean logic. Recently, we have begun exploring the application of quantum probability to problems in social and behavioral science that appear puzzling or difficult to understand from a classical probability framework.