Charles Yuan

I’m a Ph.D. candidate at MIT CSAIL working with Prof. Michael Carbin. Here’s my CV.

I study how to program a quantum computer to practically realize quantum algorithms. Quantum computers promise up to exponential 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. My research builds a new software stack of languages, libraries, and compilers to realize quantum algorithms on hardware.

I’m on the academic job market this year. Please reach out if you’re hiring!

You can email me at ude.tim@nauyselrahc or find me on GitHub, LinkedIn, or Scholar.

I worked as a Research Intern at Google Quantum AI in Summer 2024. I also used to work as a developer at Hudson River Trading. Before that, I studied at Carnegie Mellon and interned at Two Sigma, Airbnb, and Google.

Selected Publications

The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation.
Charles Yuan, Michael Carbin.
Appeared in PLDI 2024. (arXiv) (BibTeX) (video)

Highlight

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.

Quantum Control Machine: The Limits of Control Flow in Quantum Programming.
Charles Yuan, Agnes Villanyi, Michael Carbin.
Appeared in OOPSLA 2024. (arXiv) (BibTeX) (video) (press)

Highlight

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.

Tower: Data Structures in Quantum Superposition.
Charles Yuan, Michael Carbin.
Appeared in OOPSLA 2022. (arXiv) (BibTeX) (video)
Distinguished Artifact Award.

Highlight

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.

Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs.
Charles Yuan, Chris McNally, Michael Carbin.
Appeared in POPL 2022. (arXiv) (BibTeX) (video) (press)

Highlight

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.

Additional Publications

Codesign of Error-Correcting Codes and Modular Chiplets in the Presence of Defects.
Sophia Lin, Joshua Viszlai, Kaitlin Smith, Gokul Ravi, Charles Yuan, Frederic Chong, Benjamin Brown.
Appeared in ASPLOS 2024. (arXiv)

Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming.
Eric Atkinson, Charles Yuan, Guillaume Baudart, Louis Mandel, Michael Carbin.
Appeared in OOPSLA 2022. (arXiv)

Statically Bounded-Memory Delayed Sampling for Probabilistic Streams.
Eric Atkinson, Guillaume Baudart, Louis Mandel, Charles Yuan, Michael Carbin.
Appeared in OOPSLA 2021. (arXiv)

Preprints and Peer-Reviewed Workshop Papers

Expressing and Analyzing Quantum Algorithms with Qualtran.
Matthew Harrigan, Tanuj Khattar, Charles Yuan, Anurudh Peduri, Noureldin Yosri, Fionn Malone, Ryan Babbush, Nicholas Rubin.
Preprint, 2024. (arXiv)

Analyzing Quantum Programs Using the Power of Interaction.
Agnes Villanyi, Charles Yuan, Chris McNally.
Presented at PLanQC at ICFP 2022. (paper) (video)

Probabilistic Inference for Quantum Programs.
Charles Yuan, Yipeng Huang, Michael Carbin.
Presented at I2Q at ISCA 2021. (paper)

BLT: Exact Bayesian Inference with Distribution Transformers.
Charles Yuan, Jan Hoffmann.
Technical report, 2019. (paper)
Allen Newell Award (Best Undergraduate Thesis).