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Xiatian Xu

Publications and source records attributed to Xiatian Xu.

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Ytterbium lattice clock with uncertainty of $1.1\times 10^{-18}$ and instability of low $10^{-19}$

We report an optical lattice clock based on $^{171}$Yb atoms with a total systematic uncertainty of $1.1\times 10^{-18}$. In-vacuum buildup cavity was employed to enhance the lattice light power. Differential frequency measurement between two identical clocks facilitate the evaluation of systematic shifts. Synchronous comparison of the two clocks reached a stability level of $2.7\times 10^{-19}$ in an averaging time of 216,000 s. The magic frequency $ν_{\mathrm{zero}}$ was determined to be 394 798 258.3(1) MHz. Under typical operating conditions, the lattice light shift is controlled at an uncertainty level of $3\times 10^{-19}$. The blackbody radiation (BBR) shield which is placed in vacuum provides a well-characterized BBR environment, enabling an uncertainty contribution of $8.7\times 10^{-19}$ from the BBR Stark shift. Other systematic shifts have also been evaluated. The two clocks will be used for remote frequency comparisons between Shanghai and Wuhan.

physics.atom-ph

Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-term Quantum Hardware

This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. Leveraging a distributed framework, we address the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, which are hindered by limited qubit counts and high error rates. Our approach decomposes large QAOA problems into smaller subproblems, distributing them across multiple Quantum Processing Units (QPUs) to enhance scalability and performance. The noise-aware strategy incorporates error mitigation techniques to optimize qubit fidelity and gate operations, ensuring reliable quantum computations. We evaluate the efficacy of our framework using the HamilToniQ Benchmarking Toolkit, which quantifies the performance across various quantum hardware configurations. The results demonstrate that our distributed QAOA framework achieves significant improvements in computational speed and accuracy, showcasing its potential to solve complex optimization problems efficiently in the NISQ era. This work sets the stage for advanced algorithmic strategies and practical quantum system enhancements, contributing to the broader goal of achieving quantum advantage.

quant-ph