activity
20172022
most citedFrequency-Dependent Squeezed Vacuum Source for Broadband Quantum Noise Reduction in Advanced Gravitational-Wave Detectors

103 citations · 131 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AR20224 cited

A Real Time 1280x720 Object Detection Chip With 585MB/s Memory Traffic

Kuo-Wei Chang, Hsu-Tung Shih, Tian-Sheuan Chang +4

Memory bandwidth has become the real-time bottleneck of current deep learning accelerators (DLA), particularly for high definition (HD) object detection. Under resource constraints…

quant-ph2022

Direct parameter estimations from machine-learning enhanced quantum state tomography

Hsien-Yi Hsieh, Jingyu Ning, Yi-Ru Chen +4

With the capability to find the best fit to arbitrarily complicated data patterns, machine-learning (ML) enhanced quantum state tomography (QST) has demonstrated its advantages in…

physics.ins-det20227 cited

Improving the stability of frequency dependent squeezing with bichromatic control of filter cavity length, alignment and incident beam pointing

Yuhang Zhao, Eleonora Capocasa, Marc Eisenmann +21

Frequency dependent squeezing is the main upgrade for achieving broadband quantum noise reduction in upcoming observation runs of gravitational wave detectors. The proper frequency…

astro-ph.IM2020103 cited

Frequency-Dependent Squeezed Vacuum Source for Broadband Quantum Noise Reduction in Advanced Gravitational-Wave Detectors

Yuhang Zhao, Naoki Aritomi, Eleonora Capocasa +20

The astrophysical reach of current and future ground-based gravitational-wave detectors is mostly limited by quantum noise, induced by vacuum fluctuations entering the detector out…

quant-ph2020

Carrying an arbitrarily large amount of information using a single quantum particle

Li-Yi Hsu, Ching-Yi Lai, You-Chia Chang +2

Theoretically speaking, a photon can travel arbitrarily long before it enters into a detector, resulting a click. How much information can a photon carry? We study a bipartite asym…

physics.ao-ph2019

Learning the Representations of Moist Convection with Convolutional Neural Networks

Shih-Wen Tsou, Chun-Yian Su, Chien-Ming Wu

The representations of atmospheric moist convection in general circulation models have been one of the most challenging tasks due to its complexity in physical processes, and the i…