From the 1 of 8 linked papers with an AI index.
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Efficient Lindbladian Learning from Constant-Time Pauli Responses
Jiaxing Song, Yukun Zhang, Xiao Yuan +1
The paper presents efficient methods for learning the generator (Lindbladian) of open many‑body quantum systems from short‑time local Pauli response data, resolving coherent‑dissip…
Efficient Noisy Quantum State and Process Tomography
Chenyang Li, Shengxin Zhuang, Yukun Zhang +4
Efficiently characterizing large quantum states and processes is a central yet notoriously challenging task in quantum information science, as conventional tomography methods typic…
Heisenberg-Limited Quantum Eigenvalue Estimation for Non-normal Matrices
Yukun Zhang, Yusen Wu, Xiao Yuan
Estimating the eigenvalues of non-normal matrices is a foundational problem with far-reaching implications, from modeling non-Hermitian quantum systems to analyzing complex fluid d…
Classical Algorithms for Hamiltonian Dynamics Mean Value and Guided Local Hamiltonian Problem
Yusen Wu, Yukun Zhang, Chuan Wang +1
The efficient simulation of quantum dynamics and ground states is a central challenge in physics and a key frontier for quantum advantage. While short-time evolution in one-dimensi…
Measuring Less to Learn More: Quadratic Speedup in learning Nonlinear Properties of Quantum Density Matrices
Yukun Zhang, Yusen Wu, You Zhou +1
A fundamental task in quantum information science is to measure nonlinear functionals of quantum states, such as . Intuitively, one expects that computing a $k…
Hamiltonian Dynamics Learning: A Scalable Approach to Quantum Process Characterization
Yusen Wu, Yukun Zhang, Chuan Wang +1
Quantum process characterization is a fundamental task in quantum information processing, yet conventional methods, such as quantum process tomography, require prohibitive resource…