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Senrui Chen

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#machine learning Conference Feb 2026

Instance-optimal high-precision shadow tomography with few-copy measurements: A metrological approach

We study the sample complexity of shadow tomography in the high-precision regime under realistic measurement constraints. Given an unknown $d$-dimensional quantum state $\rho$ and a known set of observables $\{O_i\}_{i=1}^m$, the goal is to estimate expectation values $\{\mathrm{tr}(O_i\rho)\}_{i=1}^m$ to accuracy $\ep...

Senrui Chen, W. Gong, Sisi Zhou · 10 citations
#machine learning Preprint Sep 2026

When are bosonic Gaussian states classical to learn?

A fundamental question in physics is: When does classical behavior emerge from quantum systems? Bosonic Gaussian states provide a natural setting to explore this quantum-classical boundary, as they capture both the classical field behavior and the intrinsic quantum nature of light. Here, we address this problem from a...

Senrui Chen, A. A. Mele, Francesco Anna Mele et al. · 0 citations
Preprint Aug 2026

Near-optimal quantum metrology with few-qubit measurements

Quantum metrology, which addresses parameter estimation in quantum systems, has broad applications across science and technology. Conventional metrology protocols for multi-qubit states in the multi-parameter regime typically require highly complex quantum measurements, leading to substantial quantum-resource costs. In...

Liang Mao, Senrui Chen, Hsin-Yuan Huang et al. · 0 citations
Open access Aug 2024

Entanglement-enhanced learning of quantum processes at scale

Learning unknown noise processes in quantum systems reveals their physical origin and informs error suppression, mitigation, and correction. Characterizing a general quantum process requires exponentially many parameters inferred from noncommuting measurements. Because these measurements cannot be performed simultaneou...

A. Seif, Senrui Chen, Swarnadeep Majumder et al. · 12 citations

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