I am a Postdoctoral Fellow at Portland State University under the supervision of Fang Song. Previously I was a postdoc at Rice University under the supervision of Nai-Hui Chia. I earned a PhD in Computer Science at UT Austin while advised by Scott Aaronson. Prior to that, I received a Bachelor of Science in Engineering from Cornell University in both Computer Science and Engineering Physics.

Research Interests

My focus is on the application of learning theory to quantum problems. That is, given some unknown quantum system, try and learn it, under varying definitions of the word “learn”. During my PhD, I focused on leveraging properties of the stabilizer formalism to tackle topics such as tomography, PAC/SQ/Agnostic learning, property testing, and pseudorandomness. I am additionally broadly interested in quantum information, quantum complexity theory, and theoretical computer science.

Publications [Author Order is Alphabetical unless specified by an asterisk (*)]

  1. Tolerant Testing of Stabilizer States with Mixed State Inputs

    Vishnu Iyer, Daniel Liang
    [arXiv]

  2. Quantum State Learning Implies Circuit Lower Bounds

    Nai-Hui Chia, Daniel Liang, Fang Song
    [arXiv] [TQC 2024]

  3. Agnostic Tomography of Stabilizer Product States

    Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang
    [arXiv]

  4. Pseudoentanglement Ain’t Cheap

    Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang
    [arXiv] [TQC 2024]

  5. Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates

    Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang
    [arXiv] [QIP 2024]

  6. Improved Stabilizer Estimation via Bell Difference Sampling

    Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang
    Proceedings of the 56th Annual ACM Symposium on Theory of Computing (STOC 2024)
    [arXiv] [QIP 2024] [STOC 2024]

  7. Clifford Circuits can be Properly PAC Learned if and only if RP=NP

    Daniel Liang
    Quantum 7, 1036 – 2023
    [arXiv] [Quantum]

  8. Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom

    Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang
    14th Innovations in Theoretical Computer Science Conference (ITCS 2023)
    ITCS 2023 Best Student Paper Award
    [arXiv] [ITCS 2023]

  9. On the Hardness of PAC-learning stabilizer States with Noise

    Aravind Gollakota, Daniel Liang
    Quantum 6, 640 – 2022
    [arXiv] [Quantum]

  10. * Investigating quantum approximate optimization algorithms under bang-bang protocols

    Daniel Liang, Li Li, Stefan Leichenauer
    Physical Review Research 2 (3) – 2020
    [arXiv] [PRR]

  11. * Simulation of qubit quantum circuits via Pauli propagation

    Patrick Rall, Daniel Liang, Jeremy Cook, William Kretschmer
    Physical Review A 99 (6) – 2019
    [arXiv] [PRA]

Dissertation