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Daniel Liang
Daniel Liang
Verified email at rice.edu
Title
Cited by
Cited by
Year
Simulation of qubit quantum circuits via Pauli propagation
P Rall, D Liang, J Cook, W Kretschmer
Physical Review A 99 (6), 062337, 2019
452019
On the hardness of pac-learning stabilizer states with noise
A Gollakota, D Liang
Quantum 6, 640, 2022
182022
Investigating quantum approximate optimization algorithms under bang-bang protocols
D Liang, L Li, S Leichenauer
Physical Review Research 2 (3), 033402, 2020
172020
Efficient learning of quantum states prepared with few non-clifford gates
S Grewal, V Iyer, W Kretschmer, D Liang
arXiv preprint arXiv:2305.13409, 2023
162023
Improved stabilizer estimation via bell difference sampling
S Grewal, V Iyer, W Kretschmer, D Liang
Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 1352-1363, 2024
152024
Low-stabilizer-complexity quantum states are not pseudorandom
S Grewal, V Iyer, W Kretschmer, D Liang
arXiv preprint arXiv:2209.14530, 2022
92022
Clifford Circuits can be Properly PAC Learned if and only if
D Liang
Quantum 7, 1036, 2023
62023
Agnostic Tomography of Stabilizer Product States
S Grewal, V Iyer, W Kretschmer, D Liang
arXiv preprint arXiv:2404.03813, 2024
12024
Pseudoentanglement Ain't Cheap
S Grewal, V Iyer, W Kretschmer, D Liang
arXiv preprint arXiv:2404.00126, 2024
12024
Quantum State Learning Implies Circuit Lower Bounds
NH Chia, D Liang, F Song
arXiv preprint arXiv:2405.10242, 2024
2024
Efficient learning of quantum states prepared with few non-clifford gates ii: Single-copy measurements
S Grewal, V Iyer, W Kretschmer, D Liang
arXiv preprint arXiv:2308.07175, 2023
2023
On computationally efficient learning for stabilizers and beyond
DY Liang
2023
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Articles 1–12