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Guang-Can Guo

Researcher at University of Science and Technology of China

Publications -  545
Citations -  15193

Guang-Can Guo is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Quantum entanglement & Qubit. The author has an hindex of 53, co-authored 545 publications receiving 11582 citations. Previous affiliations of Guang-Can Guo include Center for Excellence in Education & Chinese Academy of Sciences.

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Demonstrating Quantum Coherence and Metrology that is Resilient to Transversal Noise.

TL;DR: This work experimentally investigates the resilient effect of quantum coherence in a photonic Greenberger-Horne-Zeilinger state under Markovian bit-flip noise, and explores its applications in a noisy metrology scenario, highlighting the important role of passive control in noisy quantum hardware.
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Theory of free space coupling to high-Q whispering gallery modes

TL;DR: This work finds that the asymmetric and peak like line shapes instead of the Lorentz-shape dip are universal in transmission spectra due to multi-wave interference, and the coupling efficiency cannot be estimated from the absolute depth of the dip.
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Experimental demonstration of quantum contextuality with nonentangled photons

TL;DR: In this article, the authors present an experimental test of quantum contextuality by using two-photon product states and show that the noncontextual hidden-variable theories are violated by nonentangled states in spite of the local hiddenvariable theories can be violated or not.
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A gate defined quantum dot on the two-dimensional transition metal dichalcogenide semiconductor WSe2

TL;DR: In this article, the authors used standard semiconductor fabrication techniques to demonstrate quantum confined structures on transition metal dichalcogenides (TMDCs) with tunnel barriers defined by electric fields, thereby eliminating the edge states induced by etching steps.
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Reconstruction of a Photonic Qubit State with Reinforcement Learning

TL;DR: In this paper, a semi-quantum reinforcement learning approach is employed to adapt one qubit state, an agent, to an unknown quantum state by successive single-shot measurements and feedback, in order to achieve maximum overlap.