activity
19982023
most citedA quantum network of clocks

767 citations · 4.4k across the 70 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

quant-ph2022★ 8 cited

Direct comparison of two spin squeezed optical clocks below the quantum projection noise limit

John M Robinson, Maya Miklos, Yee Ming Tso +5

Building scalable quantum systems that demonstrate genuine performance enhancement based on entanglement is a major scientific goal for fields including computing, networking, simu…

physics.atom-ph2022★ 62 cited

Evaluation of lattice light shift at low 10 uncertainty for a shallow lattice Sr optical clock

Kyungtae Kim, Alexander Aeppli, Tobias Bothwell +1

A Wannier-Stark optical lattice clock has demonstrated unprecedented measurement precision for optical atomic clocks. We present a systematic evaluation of the lattice light shift,…

physics.optics2022★ 38 cited

Excess noise and photo-induced effects in highly reflective crystalline mirror coatings

Jialiang Yu, Dhruv Kedar, Sebastian Häfner +9

Thermodynamically induced length fluctuations of high-reflectivity mirror coatings put a fundamental limit on sensitivity and stability of precision optical interferometers like gr…

quant-ph2022★ 3 cited

Accelerating Progress Towards Practical Quantum Advantage: The Quantum Technology Demonstration Project Roadmap

Paul Alsing, Phil Battle, Joshua C. Bienfang +32

Quantum information science and technology (QIST) is a critical and emerging technology with the potential for enormous world impact and is currently invested in by over 40 nations…

cs.LG2022★ 1 cited

Stochastic Gradient Descent without Full Data Shuffle

Lijie Xu, Shuang Qiu, Binhang Yuan +9

Stochastic gradient descent (SGD) is the cornerstone of modern machine learning (ML) systems. Despite its computational efficiency, SGD requires random data access that is inherent…

cs.LG2022★ 5 cited

ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization

Langzhang Liang, Zenglin Xu, Zixing Song +3

Graph Neural Networks (GNNs) have attracted much attention due to their ability in learning representations from graph-structured data. Despite the successful applications of GNNs…