I am a Postdoctoral Reseacher jointly supervised by Dr. Nai-Hui Chia at Rice University and Dr. Fang Song at Portland State University. 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 (*)]
Tolerant Testing of Stabilizer States with Mixed State Inputs
Vishnu Iyer, Daniel Liang
[arXiv]Quantum State Learning Implies Circuit Lower Bounds
Nai-Hui Chia, Daniel Liang, Fang Song
[arXiv] [TQC 2024]Agnostic Tomography of Stabilizer Product States
Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang
[arXiv]Pseudoentanglement Ain’t Cheap
Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang
[arXiv] [TQC 2024]Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates
Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang
[arXiv] [QIP 2024] [IPAM]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]Clifford Circuits can be Properly PAC Learned if and only if RP=NP
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]On the Hardness of PAC-learning stabilizer States with Noise
Aravind Gollakota, Daniel Liang
Quantum 6, 640 – 2022
[arXiv] [Quantum]* Investigating quantum approximate optimization algorithms under bang-bang protocols
Daniel Liang, Li Li, Stefan Leichenauer
Physical Review Research 2 (3) – 2020
[arXiv] [PRR]* 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
On Computationally Efficient Learning for Stabilizers and Beyond