Morgridge Hall, UW–Madison
I’m an assistant professor of computer science at UW–Madison, where I am a member of the madPL and quantum computing groups.
I’m always on the lookout for great Ph.D. students. If you’re interested, I encourage you to understand my approach to advising, read my work, learn about Madison, and apply.
Contact Email ude.csiw.sc@nauyselrahc or find me in Morgridge 7663.
Research My group studies how to program a quantum computer to realize quantum algorithms. Quantum computers promise incredible speedups for tasks such as simulation, search, and optimization. However, achieving this speedup in practice forces us to fundamentally rethink the abstractions, such as data structures and control flow, that we use to write algorithms as programs. Our research thus builds a software stack of languages and compilers to work with and reason about quantum information.
My work is supported by the U.S. National Science Foundation.
Teaching I teach CS 704 on programming languages in Fall 2026.
Service I serve as Program Chair of PLanQC 2027 and Associate Editor for ACM Transactions on Quantum Computing. I am on the PC for POPL 2027 and PLDI 2026.
Advisees Shengyuan Yang, Daksh Shami, Gunagya Singh Mamak.
Biographical Here are my CV, Google Scholar, and GitHub. I did my Ph.D. at MIT CSAIL with Michael Carbin. I’ve worked at Google Quantum AI and Hudson River Trading.
How do we practically implement quantum algorithms for linear algebra given that they cannot efficiently store large datasets in memory? We present a language to express and manipulate quantum representations of matrices as high-level programs, compile them to correct circuits, and estimate and optimize their resource usage.
What limits how fast a program can run on a quantum computer that uses an error-correcting code as needed in practice? We show how the quantum version of an if-statement can introduce an asymptotic slowdown that diminishes the speedup of an algorithm, and present an optimizing compiler that recovers efficient programs.
Can we program a quantum computer using the same abstractions for control flow, such as conditional branches and loops, as on a classical computer? We prove that the answer is no – this would destroy the speedup of the quantum algorithm – and present a new instruction set for correctly realizing control flow in quantum algorithms.
How do we implement a data structure, such as a list, set, or dictionary, on a quantum computer? We present a programming language for working with pointers stored in the states of qubits in superposition. Using it, we build a quantum library of data structures that developers can invoke to realize quantum algorithms that rely on them.
How do we reason about the impact of entanglement, or “spooky action at a distance,” on the correctness of a quantum program? We present a language and type system for a concept of purity that identifies unintuitive behavior caused by entangled quantum data, and illustrate how this system can catch bugs in quantum programs.
