5 papers
Normalizing Flow-Based Bayesian Parameter Estimation for Noisy Quantum States
Hsien-Yi Hsieh, Yuan-Ting Liu, Juan Camilo Rodrıguez +5
To extract complete information about experimental noisy quantum states, as well as correlations among different physical parameters, we develop an efficient machine learning-assis…
Monitoring Beam Splitter Entanglement using Quantumness
Hua-Li Chen, Hsien-Yi Hsieh, Chien-Ming Wu +2
We report on an experiment in which two independent squeezed vacuum states get entangled by mixing them with a balanced beam splitter. We follow standard practice and use an insepa…
Machine Learning for Quantum State Tomography: Robust Covariance Matrix Estimation for Squeezed Vacuum States with Thermal Noise
Juan Camilo Rodrıguez, Hsien-Yi Hsieh, Hua-Li Chen +3
We present a supervised machine learning-based method using convolutional neural networks to estimate the covariance matrix of Gaussian quantum states in the presence of thermal no…
Machine Learning Enhanced Quantum State Tomography on FPGA
Hsun-Chung Wu, Hsien-Yi Hsieh, Zhi-Kai Xu +7
Machine learning techniques have opened new avenues for real-time quantum state tomography (QST). In this work, we demonstrate the deployment of machine learning-based QST onto edg…
Neural Network Enhanced Single-Photon Fock State Tomography
Hsien-Yi Hsieh, Yi-Ru Chen, Jingyu Ning +7
Even though heralded single-photon sources have been generated routinely through the spontaneous parametric down conversion, vacuum and multiple photon states are unavoidably invol…