About Me
I am Ellie Vogel, an Electrical and Computer Engineering and Computer Science double major at Duke University. I am involved in the Society of Women Engineers, as well as Duke’s Technology Scholars program. I am initially from Salt Lake City, Utah, and enjoy skiing, mountain biking, and fishing in my free time. My main research interests involve quantum computing and computer architecture.
About My Mentor
Dr. Margaret Martonosi is the Hugh Trumbull Adams ‘35 Professor of Computer Science at Princeton University. She is known for her contributions to computer architecture and hardware-software interface issues in both classical and quantum computing.
Dr. Martonosi started as a faculty member in the Department of Electrical Engineering (now Electrical and Computer Engineering) in 1994. In 2010, she moved to the Computer Science department. Her work has garnered numerous awards, including being named a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). In addition to her research and teaching, she has held leadership roles such as serving on the Computing Research Association (CRA) board and participating in initiatives to advance computing research and education. Dr. Martonosi has authored or co-authored numerous research papers, contributing significantly to the advancement of technology and knowledge in computer science and engineering.
About My Project
This summer, I am working on CutQC: an open source code repository for quantum circuit cutting. The current code repository utilizes a single-node, multi-threaded implementation for the classical post-processing step. In my work, we enhance the reconstruction process. We contribute to CutQC a multi-node, single-threaded implementation of the classical post-processing step, as well as a multi-GPU implementation, utilizing PyTorch. We execute circuits of up to 35 qubits on 10- and 15- qubit QCs, and examine the reconstruction runtime. We utilize single-node, multi-threaded computation as a baseline, and then compare the results to the runtimes of our contributed workflows.