Making Trapped-Ion Entangling Gates Robust to Optical Crosstalk

As part of the Scalable Quantum Research Lab I work on designing Mølmer-Sørensen (MS) style entangling gates that are robust to optical crosstalk, or laser-ion addressing error. We are currently working with the Quantum Scientific Computing Open User Testbed (QSCOUT) team at Sandia National Lab to investigate how the choice of vibrational mode of the ion string used to run the MS gate can provide protection against crosstalk error. I am also analyzing how to use simultaneous coupling to multiple motional modes to combat crosstalk error.

Analog Quantum Simulation of Open Quantum Systems

As part of the Scalable Quantum Research Lab and in collaboration with UW Professor Rahul Trivedi I am working on a project on analog quantum simulation of open quantum systems. We have proven error bounds on a method of simulating arbitrary Lindbladians using engineered dissipation and have furthermore proved that if the Lindbladian being simulated is the sum of local terms on a lattice, the error in simulated local observables does not scale with the size of the system due to the finite speed of information propagation (Lieb-Robinson bounds). We furthermore prove that no classical algorithm can perform such a simulation as efficiently as a quantum simulator, even given common restrictions on the Lindbladian like locality in 2 dimensions. We prove this by showing that a quantum computation can be encoded in the fixed-point of a Lindbladian acting with only local terms on a 2D lattice of sites; thus if a classical algorithm could simulate local observables in the fixed points of arbitrary 2D local Lindbladians, such as those studied in condensed matter physics, it could also perform arbitrary quantum computations, which we expect to be impossible, We published a pre-print in April 2024: arXiv:2404.11081.

Analyzing X-Ray Emission Spectra with Unsupervised Machine Learning

As part of the UW Seidler Lab I worked with (then) graduate student Samantha Tetef on characterizing the chemically-relevant information encoded in the x-ray emission spectra of organophosphorous molecules. I first wrote a software pipeline that searched, downloaded, and sorted molecular structure descriptions from the PubChem database and calculated x-ray emission and absorption spectra for the molecules using the NWChem software package. We then sorted the unlabeled spectra using unsupervised machine learning algorithms. We found that the groups corresponded to chemical properties of the molecules, indicating that information on those chemical properties is encoded in the spectra and can be extracted without prior information. My work on this project was funded by the Washington NASA Space Grant during the period of June-August 2021. We published a paper on the research in July 2022.

Automatically Extracting X-Ray Emission Spectra from Data at the Advanced Photon Source

As a research aide in the x-ray spectroscopy group at the Advanced Photon Source at Argonne National Lab I worked on automating the extraction of x-ray spectra from the raw data coming from the 2D pixel array detectors (PADs) on the beamline. On the beamline, x-rays coming from a material being interrogated pass through a crystal analyzer before hitting the PAD. Different frequencies of x-rays are refracted at different angles by the crystal and so hit the PAD at different positions, allowing the frequencies to be deduced from the image captured by the PAD. I worked on writing a software package called pyAXEAP (the Argonne X-ray Emission Analysis Package in Python) that can automatically detect regions of interest (ROIs) in the PAD image, make calibrations from calibration images, and output spectra from experimental images. I mostly worked remotely as a student at the UW, but during June 2022 I worked on-site at the APS synchrotron.