Background:The masses of-2500 nuclei have been measured experimentally;however,>7000 isotopes are predicted to exist in the nuclear landscape from H(Z=1)to Og(Z=118)based on various theoretical calculations.Explori...Background:The masses of-2500 nuclei have been measured experimentally;however,>7000 isotopes are predicted to exist in the nuclear landscape from H(Z=1)to Og(Z=118)based on various theoretical calculations.Exploring the mass of the remaining isotopes is a popular topic in nuclear physics.Machine learning has served as a powerful tool for learning complex representations of big data in many fields.Purpose:We use Light Gradient Boosting Machine(LightGBM),which is a highly efficient machine learning algorithm,to predict the masses of unknown nuclei and to explore the nuclear landscape on the neutron-rich side from learning the measured nuclear masses.Methods:Several characteristic quantities(e.g.,mass number and proton number)are fed into the LightGBM algorithm to mimic the patterns of the residual δ(Z,A)between the experimental binding energy and the theoret-ical one given by the liquid-drop model(LDM),Duflo–Zucker(DZ,also dubbed DZ28)mass model,finite-range droplet model(FRDM,also dubbed FRDM2012),as well as the Weizsacker–Skyrme(WS4)model to refine these mass models.Results:By using the experimental data of 80%of known nuclei as the training dataset,the root mean square devia-tions(RMSDs)between the predicted and the experimental binding energy of the remaining 20%are approximately 0.234±0.022,0.213±0.018,0.170±0.011,and 0.222±0.016 MeV for the LightGBM-refined LDM,DZ model,WS4 model,and FRDM,respectively.These values are approximately 90%,65%,40%,and 60%smaller than those of the corresponding origin mass models.The RMSD for 66 newly measured nuclei that appeared in AME2020 was also significantly improved.The one-neutron and two-neutron separation energies predicted by these refined models are consistent with several theoretical predictions based on various physical models.In addition,the two-neutron separation energies of several newly measured nuclei(e.g.,some isotopes of Ca,Ti,Pm,and Sm)pre-dicted with LightGBM-refined mass models are also in good agreement with the latest experimental data.Conclusions:LightGBM can be used to refine theoretical nuclear mass models and predict the binding energy of unknown nuclei.Moreover,the correlation between the input characteristic quantities and the output can be inter-preted by SHapley additive exPlanations(a popular explainable artificial intelligence tool),which may provide new insights for developing theoretical nuclear mass models.展开更多
Recent advances in quantum technology have led to the development and the manufacturing of programmable quantum annealers that promise to solve certain combinatorial optimization problems faster than their classical c...Recent advances in quantum technology have led to the development and the manufacturing of programmable quantum annealers that promise to solve certain combinatorial optimization problems faster than their classical counterparts.Semi-supervised learning is a machine learning technique that makes use of both labeled and unlabeled data for training,which enables a good classifier with only a small amount of labeled data.In this paper,we propose and theoretically analyze a graph-based semi-supervised learning method with the aid of the quantum annealing technique,which efficiently utilizes the quantum resources while maintaining good accuracy.We illustrate two classification examples,suggesting the feasibility of this method even with a small portion(30%) of labeled data involved.展开更多
Following publication of the original article,Formula(2)is missing and Fig.11,Fig.9 are identical.The original article has been corrected and the Publisher apologized to the authors and the readers for the inconve-nie...Following publication of the original article,Formula(2)is missing and Fig.11,Fig.9 are identical.The original article has been corrected and the Publisher apologized to the authors and the readers for the inconve-nience caused by this error.展开更多
Deep neural networks(DNNs)and auto differentiation have been widely used in computational physics to solve variational problems.When a DNN is used to represent the wave function and solve quantum many-body problems us...Deep neural networks(DNNs)and auto differentiation have been widely used in computational physics to solve variational problems.When a DNN is used to represent the wave function and solve quantum many-body problems using variational optimization,various physical constraints have to be injected into the neural network by construction to increase the data and learning efficiency.We build the unitary constraint to the variational wave function using a monotonic neural network to represent the cumulative distribution function(CDF)F(x)=ʃ^(x)_(-∞)Ψ*Ψdx',.Using this constrained neural network to represent the variational wave function,we solve Schrodinger equations using auto-differentiation and stochastic gradient descent(SGD)by minimizing the violation of the trial wave function(x)to the Schrodinger equation.For several classical problems in quantum mechanics,we obtain their ground state wave function and energy with very low errors.The method developed in the present paper may pave a new way for solving nuclear many-body problems in the future.展开更多
The emerging hybrid integrated quantum photonics combines the advantages of different functional components into a single chip to meet the stringent requirements for quantum information processing.Despite the tremendo...The emerging hybrid integrated quantum photonics combines the advantages of different functional components into a single chip to meet the stringent requirements for quantum information processing.Despite the tremendous progress in hybrid integrations of III-V quantum emitters with silicon-based photonic circuits and superconducting single-photon detectors,on-chip optical excitations of quantum emitters via miniaturized lasers towards single-photon sources(SPSs)with low power consumptions,small device footprints,and excellent coherence properties is highly desirable yet illusive.In this work,we present realizations of bright semiconductor SPSs heterogeneously integrated with on-chip electrically-injected microlasers.Different from previous one-by-one transfer printing technique implemented in hybrid quantum dot(QD)photonic devices,multiple deterministically coupled QD-circular Bragg Grating(CBG)SPSs were integrated with electrically-injected micropillar lasers at one time via a potentially scalable transfer printing process assisted by the wide-field photoluminescence(PL)imaging technique.Optically pumped by electrically-injected microlasers,pure single photons are generated with a high-brightness of a count rate of 3.8 M/s and an extraction efficiency of 25.44%.Such a high-brightness is due to the enhancement by the cavity mode of the CBG,which is confirmed by a Purcell factor of 2.5.Our work provides a powerful tool for advancing hybrid integrated quantum photonics in general and boosts the developments for realizing highly-compact,energy-efficient and coherent SPSs in particular.展开更多
基金This work was supported in part by the National Science Foundation of China(Nos.U2032145,11875125,12047568,11790323,11790325,and 12075085)the National Key Research and Development Program of China(No.2020YFE0202002)the"Ten Thousand Talent Program"of Zhejiang Province(No.2018R52017).
文摘Background:The masses of-2500 nuclei have been measured experimentally;however,>7000 isotopes are predicted to exist in the nuclear landscape from H(Z=1)to Og(Z=118)based on various theoretical calculations.Exploring the mass of the remaining isotopes is a popular topic in nuclear physics.Machine learning has served as a powerful tool for learning complex representations of big data in many fields.Purpose:We use Light Gradient Boosting Machine(LightGBM),which is a highly efficient machine learning algorithm,to predict the masses of unknown nuclei and to explore the nuclear landscape on the neutron-rich side from learning the measured nuclear masses.Methods:Several characteristic quantities(e.g.,mass number and proton number)are fed into the LightGBM algorithm to mimic the patterns of the residual δ(Z,A)between the experimental binding energy and the theoret-ical one given by the liquid-drop model(LDM),Duflo–Zucker(DZ,also dubbed DZ28)mass model,finite-range droplet model(FRDM,also dubbed FRDM2012),as well as the Weizsacker–Skyrme(WS4)model to refine these mass models.Results:By using the experimental data of 80%of known nuclei as the training dataset,the root mean square devia-tions(RMSDs)between the predicted and the experimental binding energy of the remaining 20%are approximately 0.234±0.022,0.213±0.018,0.170±0.011,and 0.222±0.016 MeV for the LightGBM-refined LDM,DZ model,WS4 model,and FRDM,respectively.These values are approximately 90%,65%,40%,and 60%smaller than those of the corresponding origin mass models.The RMSD for 66 newly measured nuclei that appeared in AME2020 was also significantly improved.The one-neutron and two-neutron separation energies predicted by these refined models are consistent with several theoretical predictions based on various physical models.In addition,the two-neutron separation energies of several newly measured nuclei(e.g.,some isotopes of Ca,Ti,Pm,and Sm)pre-dicted with LightGBM-refined mass models are also in good agreement with the latest experimental data.Conclusions:LightGBM can be used to refine theoretical nuclear mass models and predict the binding energy of unknown nuclei.Moreover,the correlation between the input characteristic quantities and the output can be inter-preted by SHapley additive exPlanations(a popular explainable artificial intelligence tool),which may provide new insights for developing theoretical nuclear mass models.
文摘Recent advances in quantum technology have led to the development and the manufacturing of programmable quantum annealers that promise to solve certain combinatorial optimization problems faster than their classical counterparts.Semi-supervised learning is a machine learning technique that makes use of both labeled and unlabeled data for training,which enables a good classifier with only a small amount of labeled data.In this paper,we propose and theoretically analyze a graph-based semi-supervised learning method with the aid of the quantum annealing technique,which efficiently utilizes the quantum resources while maintaining good accuracy.We illustrate two classification examples,suggesting the feasibility of this method even with a small portion(30%) of labeled data involved.
文摘Following publication of the original article,Formula(2)is missing and Fig.11,Fig.9 are identical.The original article has been corrected and the Publisher apologized to the authors and the readers for the inconve-nience caused by this error.
基金Supported by the National Natural Science Foundation of China(12035006,12075098)the Natural Science Foundation of Hubei Province(2019CFB563)+1 种基金the Hubei Province Department of Education(D20201108)Hubei Province Department of Science and Technology(2021BLB171)。
文摘Deep neural networks(DNNs)and auto differentiation have been widely used in computational physics to solve variational problems.When a DNN is used to represent the wave function and solve quantum many-body problems using variational optimization,various physical constraints have to be injected into the neural network by construction to increase the data and learning efficiency.We build the unitary constraint to the variational wave function using a monotonic neural network to represent the cumulative distribution function(CDF)F(x)=ʃ^(x)_(-∞)Ψ*Ψdx',.Using this constrained neural network to represent the variational wave function,we solve Schrodinger equations using auto-differentiation and stochastic gradient descent(SGD)by minimizing the violation of the trial wave function(x)to the Schrodinger equation.For several classical problems in quantum mechanics,we obtain their ground state wave function and energy with very low errors.The method developed in the present paper may pave a new way for solving nuclear many-body problems in the future.
基金National Natural Science Foundation of China(62035017,12074442)National Key Research and Development Program of China(2018YFA0306103)+1 种基金Science and Technology Program of Guangzhou(202103030001)Hisilicon Technologies CO.,LIMITED and the national super-computer center in Guangzhou.
文摘The emerging hybrid integrated quantum photonics combines the advantages of different functional components into a single chip to meet the stringent requirements for quantum information processing.Despite the tremendous progress in hybrid integrations of III-V quantum emitters with silicon-based photonic circuits and superconducting single-photon detectors,on-chip optical excitations of quantum emitters via miniaturized lasers towards single-photon sources(SPSs)with low power consumptions,small device footprints,and excellent coherence properties is highly desirable yet illusive.In this work,we present realizations of bright semiconductor SPSs heterogeneously integrated with on-chip electrically-injected microlasers.Different from previous one-by-one transfer printing technique implemented in hybrid quantum dot(QD)photonic devices,multiple deterministically coupled QD-circular Bragg Grating(CBG)SPSs were integrated with electrically-injected micropillar lasers at one time via a potentially scalable transfer printing process assisted by the wide-field photoluminescence(PL)imaging technique.Optically pumped by electrically-injected microlasers,pure single photons are generated with a high-brightness of a count rate of 3.8 M/s and an extraction efficiency of 25.44%.Such a high-brightness is due to the enhancement by the cavity mode of the CBG,which is confirmed by a Purcell factor of 2.5.Our work provides a powerful tool for advancing hybrid integrated quantum photonics in general and boosts the developments for realizing highly-compact,energy-efficient and coherent SPSs in particular.
