From the 2 of 12 linked papers with an AI index.
12 papers
Quantum memory advantage for quantum process tomography
Carlos Bravo-Prieto, Weiyuan Gong, Antonio Anna Mele
The paper proves that quantum memory enables a lower query complexity for quantum process tomography than any protocol without quantum memory, establishing a provable advantage.
The log log jam in Gaussian state tomography
Sitan Chen, Weiyuan Gong, Qi Ye +1
The paper proves that any tomography protocol using Gaussian measurements on continuous‑variable systems inevitably incurs a sample complexity that scales as log log E with the sys…
Learning and Generating Mixed States Prepared by Shallow Channel Circuits
Fangjun Hu, Christian Kokail, Milan KornjaÄa +5
Learning quantum states from measurement data is a central problem in quantum information and computational complexity. In this work, we study the problem of learning to generate m…
Ansatz-Free Learning of Lindbladian Dynamics In Situ
Petr Ivashkov, Nikita Romanov, Weiyuan Gong +3
Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust si…
Instance-optimal high-precision shadow tomography with few-copy measurements: A metrological approach
Senrui Chen, Weiyuan Gong, Sisi Zhou
We study the sample complexity of shadow tomography in the high-precision regime under realistic measurement constraints. Given an unknown -dimensional quantum state and a…
Noisy Quantum Learning Theory
Jordan Cotler, Weiyuan Gong, Ishaan Kannan
We develop a framework for learning from noisy quantum experiments in which fault-tolerant devices access uncharacterized systems through noisy couplings. Introducing the complexit…