Quantum photonic sensors array with machine learning

• Main Author: Ho, Ping-Chien
• Other Authors: Muhammad Faeyz Karim
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/153131
• Institution: Nanyang Technological University

In this dissertation,we mainly focus on distributed quantum sensing which uses different sensing nodes combined to estimate a overall features of the network.Using the continuous-variable multipartite entanglement ,we achieve M times better performance of the entangled sensor network compared to classical one in terms of the precision and error probability.For noisy environment, we continue to have advantage over the classical case, albeit less so. As a result, the methods like quantum scissors and CV error correction help to improve the performance of DQS-CV under noisy and lossy cases and it can be applied to quantum protocols.A new network called hybrid-enhanced network combines two quantum-enhanced protocol and can make the system keep near-optimal sensing performance even in the noisy environment. It can raise the transmissivity from 60% to more than 90%. DQS-CV protocol can also be used to help improve machine learning which is called the supervised learning assisted by and entangled sensor network(SLAEN).SLAEN converge in less step compared to the conventional algorithm and can find better hyperplanes. It opens a new route to utilize the entanglement power in quantum sensing field.