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From the 1 of 7 linked papers with an AI index.

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20242026
most citedUnveiling Hierarchical Invariants in Multiphoton Linear Optics

1 citations · 1 across the 3 of their papers we have counts for

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7 papers

quant-ph2026

Experimental Observation of Anomalous Complementary Weak Values from Correlated Pairwise Two-State Vectors

Qian Xie, Liang Xu, Lijian Zhang

The paper demonstrates how entanglement and controlled post‑selection can be used to generate correlated two‑state vectors, allowing the observation of anomalous complementary weak…

quant-ph20261 cited

Unveiling Hierarchical Invariants in Multiphoton Linear Optics

Baichuan Yang, Hao Zhan, Minghao Mi +3

Linear optical networks driven by quantum states of light are important building blocks of photonic quantum technologies. They access large bosonic Hilbert spaces through multiphot…

quant-ph2026

Experimental Tabletop Petz recovery of a photonic qubit

Hui Li, Jinyan Chen, Yue Pan +4

The quantum information lost in open evolutions cannot be fully recovered, but partial recovery is possible. The Petz recovery map guarantees almost optimal recovery, notably if th…

quant-ph2025

Experimental benchmarking of quantum state overlap estimation strategies with photonic systems

Hao Zhan, Ben Wang, Minghao Mi +4

Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing. While various strategies using distinct quantum measurements have…

quant-ph2024

Achieving the Multi-parameter Quantum Cramér-Rao Bound with Antiunitary Symmetry

Ben Wang, Kaimin Zheng, Qian Xie +3

The estimation of multiple parameters is a ubiquitous requirement in many quantum metrology applications. However, achieving the ultimate precision limit, i.e. the quantum Cramér-…

quant-ph2024

Progress and Perspectives on Weak-value Amplification

Liang Xu, Lijian Zhang

Weak-value amplification (WVA) is a metrological protocol that effectively amplifies ultra-small physical effects, making it highly applicable in the fields of quantum sensing and…