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Yuan Su
Biography
Yuan Su is a Senior Research Scientist at the AWS Center for Quantum Computing. Previously, he was a researcher at Microsoft, Google, and a postdoc at Caltech. He studies the theoretical side of quantum simulation, with the goal of understanding and optimizing the performance of quantum algorithms for simulating quantum physics.
Su obtained his doctorate degree in Computer Science from the University of Maryland. His academic adviser was Andrew Childs. Contributions from his thesis “Algorithms for Quantum Simulation: Design, Analysis, Implementation, and Application” were recognized by the 2020 Larry S. Davis Doctoral Dissertation Award.
He is a recipient of the 2019 Google PhD Fellowship in quantum computing and the 2022 Reviewer of the Year Award from npj Quantum Information.
Interests
- Quantum algorithms
- Quantum simulation
- Quantum computing
Education
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PhD in Computer Science, 2020
University of Maryland
-
MSc in Applied Mathematics, 2015
Peking University
-
BEng in Computer Science, 2012
Beijing University of Posts and Telecommunications
Experience
Senior Research Scientist
Amazon
Oct 2025 –
Present
Pasadena, California
Designed and analyzed quantum algorithms for early fault-tolerant quantum computers.
Senior Researcher
Microsoft
Jun 2022 –
Oct 2025
Redmond, Washington
Designed and analyzed quantum algorithms for early fault-tolerant quantum computers.
Research Scientist
Google
Apr 2021 –
Jun 2022
Venice, California
Designed and analyzed quantum algorithms for early fault-tolerant quantum computers.
Postdoctoral Scholar Research Associate
Caltech
Jul 2020 –
Apr 2021
Pasadena, California
Designed and analyzed quantum algorithms for quantum chemistry and quantum field theory.
Research Intern
Google
May 2019 –
Aug 2019
Venice, California
Estimated the resource requirements of quantum algorithms for quantum chemistry simulation.
Research Intern
Microsoft
May 2018 –
Aug 2018
Redmond, Washington
Designed and analyzed quantum algorithms for quantum chemistry simulation.
Teaching Assistant
University of Maryland
Aug 2016 –
Dec 2016
College Park, Maryland
Served as a teaching assistant of Introduction to quantum information processing (CMSC 858K).
Graduate Research Assistant
University of Maryland
Aug 2015 –
May 2020
College Park, Maryland
Designed and analyzed quantum algorithms for simulating quantum systems.
Teaching Assistant
Peking University
Sep 2013 –
Jan 2015
Beijing, China
Served as a teaching assistant of Advanced Linear Algebra (I) and (II).
Recent Talks
Quantum algorithms for the linear system problem produce the solution $A^{-1}|b\rangle$ by querying two oracles: $O_A$ that block …
Feb 24, 2025
Raleigh Convention Center, 500 Salisbury Street, Raleigh, NC 27601 United States
Many problems in linear algebra – such as those arising from non-Hermitian physics and differential equations – can be …
Jan 18, 2024
Taipei International Convention Center, No. 1, Section 5, Xinyi Road, Taipei City 11049, Taiwan
Quantum dynamics can be simulated on a quantum computer by exponentiating elementary terms from the Hamiltonian in a sequential manner. …
Oct 2, 2023
Institute for Pure and Applied Mathematics, University of California, Los Angeles, CA 90095, United States
Quantum dynamics can be simulated on a quantum computer by exponentiating elementary terms from the Hamiltonian in a sequential manner. …
Aug 20, 2023
Ningxia University, Yinchuan City, Ningxia 750021, China (online event)
Quantum dynamics can be simulated on a quantum computer by exponentiating elementary terms from the Hamiltonian in a sequential manner. …
Apr 13, 2023
Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China (online event)
This talk, delivered in Chinese, provides an overview of quantum algorithms for general-purpose Hamiltonian simulation. Topics included …
Jun 8, 2022
Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China (online event)
The Lie-Trotter formula, together with its higher-order generalizations, provides a simple approach to decomposing the exponential of a …
Apr 16, 2021
Michigan State University, East Lansing, MI 48824, United States (online event)
We consider simulating quantum systems on digital quantum computers. We show that the performance of quantum simulation can be improved …
Feb 5, 2021
Munich Center for Quantum Science and Technology, D-80799 Munich, Germany (online event)
The Lie-Trotter formula, together with its higher-order generalizations, provides a direct approach to decomposing the exponential of a …
Oct 30, 2020
California Institute of Technology, Pasadena, CA 91125, United States (online event)
The Lie-Trotter formula, together with its higher-order generalizations, provides a direct approach to decomposing the exponential of a …
Apr 28, 2020
Simons Institute for the Theory of Computing, Berkeley, CA 94720, United States (online event)
The Lie-Trotter formula, together with its higher-order generalizations, provides a direct approach to decomposing the exponential of a …
Apr 8, 2020
Riverlane, Cambridge CB2 3BZ, United Kingdom (online event)
The Lie-Trotter formula, together with its higher-order generalizations, provides a direct approach to decomposing the exponential of a …
Jan 10, 2020
Peng Cheng Laboratory, Nanshan, Shenzhen, Guangdong 518066, China
Merged talk.
Faster quantum simulation by randomization:
Product formulas can be used to simulate Hamiltonian dynamics on a quantum …
Faster quantum simulation by randomization:
Product formulas can be used to simulate Hamiltonian dynamics on a quantum …
Jun 5, 2019
University of Maryland, College Park, MD 20742, United States
With quantum computers of significant size now on the horizon, we should understand how to best exploit their initially limited …
Jan 15, 2018
Delft University of Technology, 2628 CD Delft, the Netherlands
Featured Publications
Many problems in linear algebra require processing eigenvalues of the input matrices. As eigenvalues are different from singular values …
Guang Hao Low,
Yuan Su
The quantum linear system problem provides one of the most enticing sources of exponential quantum speedups, and its resolution …
Guang Hao Low,
Yuan Su
Quantum dynamics can be simulated on a quantum computer by exponentiating elementary terms from the Hamiltonian in a sequential manner. …
Guang Hao Low,
Yuan Su,
Yu Tong,
Minh C. Tran
Quantum many-body systems involving bosonic modes or gauge fields have infinite-dimensional local Hilbert spaces which must be …
Yu Tong,
Victor V. Albert,
Jarrod R. McClean,
John Preskill,
Yuan Su
We prove an entanglement area law for a class of 1D quantum systems involving infinite-dimensional local Hilbert spaces. This class of …
Nilin Abrahamsen,
Yuan Su,
Yu Tong,
Nathan Wiebe
We consider simulating quantum systems on digital quantum computers. We show that the performance of quantum simulation can be improved …
Yuan Su,
Hsin-Yuan Huang,
Earl T. Campbell
Simulating the dynamics of quantum systems is an important application of quantum computers and has seen a variety of implementations …
Minh C. Tran,
Yuan Su,
Daniel Carney,
Jacob M. Taylor
The Lie-Trotter formula, together with its higher-order generalizations, provides a simple approach to decomposing the exponential of a …
Andrew M. Childs,
Yuan Su,
Minh C. Tran,
Nathan Wiebe,
Shuchen Zhu
The difficulty of simulating quantum dynamics depends on the norm of the Hamiltonian. When the Hamiltonian varies with time, the …
Dominic W. Berry,
Andrew M. Childs,
Yuan Su,
Xin Wang,
Nathan Wiebe
The ability to implement the Quantum Fourier Transform (QFT) efficiently on a quantum computer facilitates the advantages offered by a …
Yunseong Nam,
Yuan Su,
Dmitri Maslov
Product formulas can be used to simulate Hamiltonian dynamics on a quantum computer by approximating the exponential of a sum of …
Andrew M. Childs,
Aaron Ostrander,
Yuan Su
We consider simulating an $n$-qubit Hamiltonian with nearest-neighbor interactions evolving for time $t$ on a quantum computer. We show …
Andrew M. Childs,
Yuan Su
Quantum computing is powerful because unitary operators describing the time-evolution of a quantum system have exponential size in …
András Gilyén,
Yuan Su,
Guang Hao Low,
Nathan Wiebe
With quantum computers of significant size now on the horizon, we should understand how to best exploit their initially limited …
Andrew M. Childs,
Dmitri Maslov,
Yunseong Nam,
Neil J. Ross,
Yuan Su
Advising
- Phuong Nam Vu, Microsoft, 2025 (co-supervised with David Williams-Young and Nathan Baker et al.)
- Christopher Kang, Microsoft, 2024
- Daniel Marti Dafcik, Microsoft, 2023 (co-supervised with Guang Hao Low)
- Yu Tong, Google, 2021 (co-supervised with Victor Albert, Jarrod McClean, and John Preskill)
Contact
- buptsuyuan@gmail.com
- 290 South Holliston Ave, Pasadena, CA 91106