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Quantum resource theory
Amortized Stabilizer Rényi Entropy of Quantum Dynamics
Unraveling the secrets of how much nonstabilizerness a quantum dynamic can generate is crucial for harnessing the power of magic …
Chengkai Zhu
,
Yu-Ao Chen
,
Zanqiu Shen
,
Zhiping Liu
,
Zhan Yu
,
Xin Wang
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DOI
Limitations of Classically Simulable Measurements for Quantum State Discrimination
In the realm of fault-tolerant quantum computing, stabilizer operations play a pivotal role, characterized by their remarkable …
Chengkai Zhu
,
Zhiping Liu
,
Chenghong Zhu
,
Xin Wang
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DOI
Retrieving Non-Linear Features from Noisy Quantum States
Accurately estimating high-order moments of quantum states is an elementary precondition for many crucial tasks in quantum computing, …
Benchi Zhao
,
Mingrui Jing
,
Lei Zhang
,
Xuanqiang-Zhao
,
Xin Wang
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DOI
Physical Implementability for Reversible Magic State Manipulation
Magic states are essential for achieving universal quantum computation. This study introduces a reversible framework for the …
Yu-Ao Chen
,
Gour Gilad
,
Xin Wang
,
Lei Zhang
,
Chenghong Zhu
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DOI
Reversible Entanglement Beyond Quantum Operations
We introduce a reversible theory of exact entanglement manipulation by establishing a necessary and sufficient condition for state …
Xin Wang
,
Yu-Ao Chen
,
Lei Zhang
,
Chenghong Zhu
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DOI
Power of Quantum Measurement in Simulating Unphysical Operations
The manipulation of quantum states through linear maps beyond quantum operations has many important applications in various areas of …
Xuanqiang-Zhao
,
Lei Zhang
,
Benchi Zhao
,
Xin Wang
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DOI
Information recoverability of noisy quantum states
Extracting classical information from quantum systems is an essential step of many quantum algorithms. However, this information could …
Xuanqiang-Zhao
,
Benchi Zhao
,
Zihan Xia
,
Xin Wang
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DOI
Symmetric distinguishability as a quantum resource
We develop a resource theory of symmetric distinguishability, the fundamental objects of which are elementary quantum information …
Robert Salzmann
,
Nilanjana Datta
,
Gilad Gour
,
Xin Wang
,
Mark M. Wilde
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