Amanda Xu
University of Wisconsin-Madison
axu44@wisc.edu
Bio
Amanda Xu is a fifth-year Ph.D. candidate at the University of Wisconsin-Madison. Her research extends ideas from programming languages and formal methods to develop a flexible compilation stack for rapidly evolving quantum hardware. She received her B.S. in Computer Science from Cornell University in 2020 and has worked at Google Quantum AI and Amazon Web Services. Her work is supported by a Cisco Systems Distinguished Graduate Fellowship.
Areas of Research
- Programming Languages
Synthesizing Quantum-Circuit Optimizers
Recent breakthroughs in quantum computing hardware have brought us closer to realizing the dream of quantum computers accelerating domains such as materials discovery, chemistry, and beyond. Further shrinking the timeline for solving useful problems with quantum computing requires a flexible compilation stack that can adapt to emerging hardware. My research develops robust tools and techniques for ensuring that programs obtain the intended output when run on arbitrary error-prone quantum computing architectures. Specifically, my focus has been on automatically synthesizing an effective and correct quantum-circuit optimizer for a given device. I first developed QUESO, an efficient approach for synthesizing correct rewrite rules given the set of operations supported by a particular device. Then I introduced a framework to unify rewrite rules with circuit resynthesis — a previously disparate technique for optimizing quantum circuits. Along with this framework, I proposed a radically simple yet effective algorithm, GUOQ, for scheduling both types of optimization in a way that exploits their synergies. These automated approaches for discovering optimizations and a good schedule for applying them outperform state-of-the-art hand-crafted alternatives. Moving forward, I envision program synthesis and verification as critical components of building a compilation stack capable of unlocking the full potential of quantum computing.