8 papers
Learning to Reconstruct Wigner Functions in Phase Space
Xinyu Tang, Yi-hsin Lin, Yan Zhu +5
Wigner function learning is a central tool for characterizing continuous variable quantum systems. A fundamental challenge in this setting is to infer a continuous phase-space func…
Foundation Model for Unified Characterization of Optical Quantum States
Xiaoting Gao, Yan Zhu, Feng-Xiao Sun +2
Machine learning methods have been used to infer specific properties of limited families of optical quantum states, but a unified model that predicts a broad range of properties fo…
Designing Shadow Tomography Protocols by Natural Language Processing
Yadong Wu, Pengfei Zhang, Ce Wang +2
Quantum circuits form a foundational framework in quantum science, enabling the description, analysis, and implementation of quantum computations. However, designing efficient circ…
Artificial intelligence for representing and characterizing quantum systems
Yuxuan Du, Yan Zhu, Yuan-Hang Zhang +8
Efficient characterization of large-scale quantum systems, especially those produced by quantum analog simulators and megaquop quantum computers, poses a central challenge in quant…
Sequence-Model-Guided Measurement Selection for Quantum State Learning
Jiaxin Huang, Yan Zhu, Giulio Chiribella +1
Characterization of quantum systems from experimental data is a central problem in quantum science and technology. But which measurements should be used to gather data in the first…
Contractive Unitary and Classical Shadow Tomography
Yadong Wu, Ce Wang, Juan Yao +3
The rapid development of quantum technology demands efficient characterization of complex quantum many-body states. However, full quantum state tomography requires an exponential n…