31 citations · 69 across the 13 of their papers we have counts for
14 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…
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…
Controlling Unknown Quantum States via Data-Driven State Representations
Yan Zhu, Tailong Xiao, Guihua Zeng +2
Accurate control of quantum states is crucial for quantum computing and other quantum technologies. In the basic scenario, the task is to steer a quantum system towards a target st…
Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models
Manwen Liao, Yan Zhu, Giulio Chiribella +1
Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologi…
Learning quantum properties from short-range correlations using multi-task networks
Ya-Dong Wu, Yan Zhu, Yuexuan Wang +1
Characterizing multipartite quantum systems is crucial for quantum computing and many-body physics. The problem, however, becomes challenging when the system size is large and the…
Quantum Process Learning Through Neural Emulation
Yan Zhu, Ya-Dong Wu, Qiushi Liu +2
Neural networks are a promising tool for characterizing intermediate-scale quantum devices from limited amounts of measurement data. A challenging problem in this area is to learn…
Efficient Learning of Continuous-Variable Quantum States
Ya-Dong Wu, Yan Zhu, Giulio Chiribella +1
The characterization of continuous-variable quantum states is crucial for applications in quantum communication, sensing, simulation and computing. However, a full characterization…
Quantum Similarity Testing with Convolutional Neural Networks
Ya-Dong Wu, Yan Zhu, Ge Bai +2
The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so fa…
Flexible learning of quantum states with generative query neural networks
Yan Zhu, Ya-Dong Wu, Ge Bai +3
Deep neural networks are a powerful tool for the characterization of quantum states. Existing networks are typically trained with experimental data gathered from the specific quant…
Quantum autoencoders for communication-efficient quantum cloud computing
Yan Zhu, Ge Bai, Yuexuan Wang +2
In the model of quantum cloud computing, the server executes a computation on the quantum data provided by the client. In this scenario, it is important to reduce the amount of qua…
Quantum Causal Unravelling
Ge Bai, Ya-Dong Wu, Yan Zhu +2
Complex processes often arise from sequences of simpler interactions involving a few particles at a time. These interactions, however, may not be directly accessible to experiments…
Efficient Algorithms for Causal Order Discovery in Quantum Networks
Ge Bai, Ya-Dong Wu, Yan Zhu +2
Given black-box access to the input and output systems, we develop the first efficient quantum causal order discovery algorithm with polynomial query complexity with respect to the…