We report a metrology scheme which measures the magnetic susceptibility of an atomic spin ensemble along the x and z directions and produces parameter estimation with precision beating the standard quantum limit.The a...We report a metrology scheme which measures the magnetic susceptibility of an atomic spin ensemble along the x and z directions and produces parameter estimation with precision beating the standard quantum limit.The atomic ensemble is initialized via one-axis spin squeezing with optimized squeezing time and parameterΦ(to be estimated)assumed as uniformly distributed between 0 and 2πwhile fixed in each estimation.One estimation ofΦcan be produced with every two magnetic susceptibility data measured along the two axes respectively,which has an imprecision scaling(1.43±0.02)/N^(0.687±0.003)with respect to the number N of the atomic spins.The measurement scheme is easy to implement and is robust against the measurement fluctuation caused by environment noise and measurement defects.展开更多
The sensitivity of optical measurement is ultimately constrained by the shot noise to the standard quantum limit.It has become a common concept that beating this limit requires quantum resources.A deep-learning neural...The sensitivity of optical measurement is ultimately constrained by the shot noise to the standard quantum limit.It has become a common concept that beating this limit requires quantum resources.A deep-learning neural network free of quantum principle has the capability of removing classical noise from images,but it is unclear in reducing quantum noise.In a coincidence-imaging experiment,we show that quantum-resource-free deep learning can be exploited to surpass the standard quantum limit via the photon-number-dependent nonlinear feedback during training.Using an effective classical light with photon flux of about 9×10^(4) photons per second,our deep-learning-based scheme achieves a 14 dB improvement in signal-to-noise ratio with respect to the standard quantum limit.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.T2121001,11934018,and U1801661)Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB28000000)+2 种基金the Key-Area Research and Development Program of GuangDong Province,China(Grant No.2018B030326001)Guangdong Provincial Key Laboratory(Grant No.2019B121203002)the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant Nos.KYTDPT20181011104202253 and 2016ZT06D348)。
文摘We report a metrology scheme which measures the magnetic susceptibility of an atomic spin ensemble along the x and z directions and produces parameter estimation with precision beating the standard quantum limit.The atomic ensemble is initialized via one-axis spin squeezing with optimized squeezing time and parameterΦ(to be estimated)assumed as uniformly distributed between 0 and 2πwhile fixed in each estimation.One estimation ofΦcan be produced with every two magnetic susceptibility data measured along the two axes respectively,which has an imprecision scaling(1.43±0.02)/N^(0.687±0.003)with respect to the number N of the atomic spins.The measurement scheme is easy to implement and is robust against the measurement fluctuation caused by environment noise and measurement defects.
基金supported by the National Key R&D Program of China(Nos.2019YFA0308700,2019YFA0308704,and 2022YFA1405000)the Innovation Program for Quantum Science and Technology(No.2021ZD0301400)+3 种基金the National Natural Science Foundation of China(Nos.11874212 and 11890704)the Program for Innovative Talents and Teams in Jiangsu(No.JSSCTD202138)the Excellent Research Program of Nanjing University(No.ZYJH002)the Natural Science Foundation of Jiangsu Province,Major Project(No.BK20212004).
文摘The sensitivity of optical measurement is ultimately constrained by the shot noise to the standard quantum limit.It has become a common concept that beating this limit requires quantum resources.A deep-learning neural network free of quantum principle has the capability of removing classical noise from images,but it is unclear in reducing quantum noise.In a coincidence-imaging experiment,we show that quantum-resource-free deep learning can be exploited to surpass the standard quantum limit via the photon-number-dependent nonlinear feedback during training.Using an effective classical light with photon flux of about 9×10^(4) photons per second,our deep-learning-based scheme achieves a 14 dB improvement in signal-to-noise ratio with respect to the standard quantum limit.
