About

I’m a PhD student in Physics at the University of Maryland, College Park. My primary research interests are in Quantum Computing - specifically, Quantum Algorithms and Quantum Complexity Theory. I’m also interested in both quantum and classical machine learning. I’m fortunate to be advised by Professor Alexey Gorshkov and Professor Andrew Childs.

My studies at UMD are supported by the NSF Graduate Research Fellowship.

Contact: here, ddhruv@umd.edu

Background

I completed my undergrad at UC Berkeley in 2019 with a B.A. in Physics and Computer Science.

While at Berkeley, I worked with Professor Birgitta Whaley towards using tensor networks for Quantum Machine Learning, and implementing our tree tensor network models on Rigetti’s Quantum Computer. I also did research in experimental particle physics with the ATLAS collaboration under Professor Marjorie Shapiro, where I worked on projects involving new inner detector designs for the LHC as well as searches for Dark Matter candidate particles.

Publications

Google Scholar

Implementing a fast unbounded quantum fanout gate using power-law interactions. arxiv:2007.00662 (with Andrew Guo, Abhinav Deshpande, Su-Kuan Chu, Zachary Eldredge, Przemyslaw Bienias, Yuan Su, Andrew Childs, and Alexey Gorshkov)
Quantum routing with Teleportation Accepted talk at QCTIP 2022 arxiv:2204.04185 (with Eddie Schoute, Aniruddha Bapat, Andrew Childs, and Alexey Gorshkov)

Projects

Deep Learning for Music Genre Classification

Professional Experience

QCB

I am the founder and former president of Quantum Computing @ Berkeley, an undergraduate club aiming to spread quantum computing knowledge and connect industry, academia, and students. I also created and taught a DeCal (student run course) on quantum computing in Fall 2018.

Software Engineering

In Summer 2017, I interned at Sonos on the Partner Integrations team, working on app development across Android, iOS, and Windows. In Summer 2018, I interned at Amazon on the AWS Rekognition team, where I worked on developing pipelines for different face detection and recognition models.