Ramping a physical parameter is one of the most common experimental protocols in studying a quantum system, and ramping dynamics has been widely used in preparing a quantum state and probing physical properties. Here,...Ramping a physical parameter is one of the most common experimental protocols in studying a quantum system, and ramping dynamics has been widely used in preparing a quantum state and probing physical properties. Here, we present a novel method of probing quantum many-body correlation by ramping dynamics. We ramp a Hamiltonian parameter to the same target value from different initial values and with different velocities, and we show that the first-order correction on the finite ramping velocity is universal and path-independent, revealing a novel quantum many-body correlation function of the equilibrium phases at the target values. We term this method as the non-adiabatic linear response since this is the leading order correction beyond the adiabatic limit. We demonstrate this method experimentally by studying the Bose-Hubbard model with ultracold atoms in three-dimensional optical lattices.Unlike the conventional linear response that reveals whether the quasi-particle dispersion of a quantum phase is gapped or gapless, this probe is more sensitive to whether the quasi-particle lifetime is long enough such that the quantum phase possesses a well-defined quasi-particle description. In the BoseHubbard model, this non-adiabatic linear response is significant in the quantum critical regime where well-defined quasi-particles are absent. And in contrast, this response is vanishingly small in both superfluid and Mott insulators which possess well-defined quasi-particles. Because our proposal uses the most common experimental protocol, we envision that our method can find broad applications in probing various quantum systems.展开更多
We present a general machine learning based scheme to optimize experimental control.The method utilizes the neural network to learn the relation between the control parameters and the control goal,with which the optim...We present a general machine learning based scheme to optimize experimental control.The method utilizes the neural network to learn the relation between the control parameters and the control goal,with which the optimal control parameters can be obtained.The main challenge of this approach is that the labeled data obtained from experiments are not abundant.The central idea of our scheme is to use the active learning to overcome this difficulty.As a demonstration example,we apply our method to control evaporative cooling experiments in cold atoms.We have first tested our method with simulated data and then applied our method to real experiments.It is demonstrated that our method can successfully reach the best performance within hundreds of experimental runs.Our method does not require knowledge of the experimental system as a prior and is universal for experimental control in different systems.展开更多
We train a neural network to identify impurities in the experimental images obtained by the scanning tunneling microscope(STM)measurements.The neural network is first trained with a large number of simulated data and ...We train a neural network to identify impurities in the experimental images obtained by the scanning tunneling microscope(STM)measurements.The neural network is first trained with a large number of simulated data and then the trained neural network is applied to identify a set of experimental images taken at different voltages.We use the convolutional neural network to extract features from the images and also implement the attention mechanism to capture the correlations between images taken at different voltages.We note that the simulated data can capture the universal Friedel oscillation but cannot properly describe the non-universal physics short-range physics nearby an impurity,as well as noises in the experimental data.And we emphasize that the key of this approach is to properly deal with these differences between simulated data and experimental data.Here we show that even by including uncorrelated white noises in the simulated data,the performance of the neural network on experimental data can be significantly improved.To prevent the neural network from learning unphysical short-range physics,we also develop another method to evaluate the confidence of the neural network prediction on experimental data and to add this confidence measure into the loss function.We show that adding such an extra loss function can also improve the performance on experimental data.Our research can inspire future similar applications of machine learning on experimental data analysis.展开更多
基金supported by Beijing Outstanding Young Scholar Programthe National Key Research and Development Program of China (2021YFA0718303, 2021YFA1400904, and 2016YFA0301501)+1 种基金the National Natural Science Foundation of China (91736208, 11974202, 61975092, 11920101004,61727819, 11934002, 11734010, and 92165203)the XPLORER Prize。
文摘Ramping a physical parameter is one of the most common experimental protocols in studying a quantum system, and ramping dynamics has been widely used in preparing a quantum state and probing physical properties. Here, we present a novel method of probing quantum many-body correlation by ramping dynamics. We ramp a Hamiltonian parameter to the same target value from different initial values and with different velocities, and we show that the first-order correction on the finite ramping velocity is universal and path-independent, revealing a novel quantum many-body correlation function of the equilibrium phases at the target values. We term this method as the non-adiabatic linear response since this is the leading order correction beyond the adiabatic limit. We demonstrate this method experimentally by studying the Bose-Hubbard model with ultracold atoms in three-dimensional optical lattices.Unlike the conventional linear response that reveals whether the quasi-particle dispersion of a quantum phase is gapped or gapless, this probe is more sensitive to whether the quasi-particle lifetime is long enough such that the quantum phase possesses a well-defined quasi-particle description. In the BoseHubbard model, this non-adiabatic linear response is significant in the quantum critical regime where well-defined quasi-particles are absent. And in contrast, this response is vanishingly small in both superfluid and Mott insulators which possess well-defined quasi-particles. Because our proposal uses the most common experimental protocol, we envision that our method can find broad applications in probing various quantum systems.
基金Supported by the Beijing Outstanding Young Scientist Program(HZ)the National Key R&D Program of China(Grant Nos.2016YFA0301600, 2016YFA0301602, and 2018YFA0307600)the National Natural Science Foundation of China(Grant Nos.11734010 and 11804203)
文摘We present a general machine learning based scheme to optimize experimental control.The method utilizes the neural network to learn the relation between the control parameters and the control goal,with which the optimal control parameters can be obtained.The main challenge of this approach is that the labeled data obtained from experiments are not abundant.The central idea of our scheme is to use the active learning to overcome this difficulty.As a demonstration example,we apply our method to control evaporative cooling experiments in cold atoms.We have first tested our method with simulated data and then applied our method to real experiments.It is demonstrated that our method can successfully reach the best performance within hundreds of experimental runs.Our method does not require knowledge of the experimental system as a prior and is universal for experimental control in different systems.
基金supported by Beijing Outstanding Scholar Programthe National Key Research and Development Program of China(Grant No. 2016YFA0301600)+3 种基金the National Natural Science Foundation of China(Grant No. 11734010)supported by a startup fund from UCSDsupported by the Fundamental Research Funds for the Central Universitiesthe Research Funds of Renmin University of China
文摘We train a neural network to identify impurities in the experimental images obtained by the scanning tunneling microscope(STM)measurements.The neural network is first trained with a large number of simulated data and then the trained neural network is applied to identify a set of experimental images taken at different voltages.We use the convolutional neural network to extract features from the images and also implement the attention mechanism to capture the correlations between images taken at different voltages.We note that the simulated data can capture the universal Friedel oscillation but cannot properly describe the non-universal physics short-range physics nearby an impurity,as well as noises in the experimental data.And we emphasize that the key of this approach is to properly deal with these differences between simulated data and experimental data.Here we show that even by including uncorrelated white noises in the simulated data,the performance of the neural network on experimental data can be significantly improved.To prevent the neural network from learning unphysical short-range physics,we also develop another method to evaluate the confidence of the neural network prediction on experimental data and to add this confidence measure into the loss function.We show that adding such an extra loss function can also improve the performance on experimental data.Our research can inspire future similar applications of machine learning on experimental data analysis.
