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Xing Ding

Researcher at University of Science and Technology of China

Publications -  32
Citations -  6162

Xing Ding is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Photon & Quantum dot. The author has an hindex of 17, co-authored 31 publications receiving 3649 citations. Previous affiliations of Xing Ding include Anhui University.

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Quantum computational advantage using photons

TL;DR: In this paper, the authors proposed to use quantum computers to perform certain tasks that are believed to be intractable to classical computers, such as Boson sampling, which is considered a strong candidate to demonstrate the capabilities of quantum computers.
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Single quantum emitters in monolayer semiconductors

TL;DR: A new class of SQEs based on excitons that are spatially localized by defects in 2D tungsten-diselenide (WSe2) monolayers is reported, which could give rise to practical advantages in quantum-information processing, such as an efficient photon extraction and a high integratability and scalability.
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On-Demand Single Photons with High Extraction Efficiency and Near-Unity Indistinguishability from a Resonantly Driven Quantum Dot in a Micropillar.

TL;DR: By s-shell pulsed resonant excitation of a Purcell-enhanced quantum dot-micropillar system, deterministically generate resonance fluorescence single photons which, at π pulse excitation, have an extraction efficiency of 66, single-photon purity of 99.1%, and photon indistinguishability of 98.5%.
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Quantum computational advantage using photons

TL;DR: Gaussian boson sampling was performed by sending 50 indistinguishable single-mode squeezed states into a 100-mode ultralow-loss interferometer with full connectivity and random matrix and sampling the output using 100 high-efficiency single-photon detectors, and the obtained samples were validated against plausible hypotheses exploiting thermal states, distinguishable photons, and uniform distribution.
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High-efficiency multiphoton boson sampling

TL;DR: In this paper, the authors used the National Natural Science Foundation of China, the Chinese Academy of Sciences, the National Fundamental Research Program, and the State of Bavaria to support the work of the authors.