The Large sky Area Multi-Object Fiber Spectroscopic Telescope（LAMOST） general survey is a spectroscopic survey that will eventually cover approximately half of the celestial sphere and collect 10 million spectra of ...The Large sky Area Multi-Object Fiber Spectroscopic Telescope（LAMOST） general survey is a spectroscopic survey that will eventually cover approximately half of the celestial sphere and collect 10 million spectra of stars, galaxies and QSOs. Objects in both the pilot survey and the first year regular survey are included in the LAMOST DR1. The pilot survey started in October 2011 and ended in June 2012, and the data have been released to the public as the LAMOST Pilot Data Release in August 2012. The regular survey started in September 2012, and completed its first year of operation in June 2013. The LAMOST DR1 includes a total of 1202 plates containing 2 955 336 spectra, of which 1 790 879 spectra have observed signalto-noise ratio（SNR） ≥ 10. All data with SNR ≥ 2 are formally released as LAMOST DR1 under the LAMOST data policy. This data release contains a total of 2 204 696 spectra, of which 1 944 329 are stellar spectra, 12 082 are galaxy spectra and 5017 are quasars. The DR1 not only includes spectra, but also three stellar catalogs with measured parameters： late A,FGK-type stars with high quality spectra（1 061 918 entries）, A-type stars（100 073 entries）, and M-type stars（121 522 entries）. This paper introduces the survey design, the observational and instrumental limitations, data reduction and analysis, and some caveats. A description of the FITS structure of spectral files and parameter catalogs is also provided.展开更多
AIM:To assess the effect of age at diabetes onset and uncontrollable high Hb A1 c levels on the development of diabetic retinopathy(DR)among Chinese type 2 diabetes mellitus(DM)patients.METHODS:This was a cross-sectio...AIM:To assess the effect of age at diabetes onset and uncontrollable high Hb A1 c levels on the development of diabetic retinopathy(DR)among Chinese type 2 diabetes mellitus(DM)patients.METHODS:This was a cross-sectional survey of diabetic patients in Subei district,China.Data covering physical measurements,fasting blood-glucose(FBG),glycosylated hemoglobin(Hb A1 c),blood lipid,urinary albumin/creatinine ratio(UACR),ocular fundus examination,and diabetes treatment records were collected.An independent sample t-test were used to analyze differences.A Logistic regression analysis was applied to study the independent risk factors of DR.RESULTS:A total of 1282 patients with type 2 DM were enrolled,and 191 cases had DR(14.9%).The age at diabetes onset,education level,alcohol consumption,Hb A1 c level,UACR level,and hypoglycemic drugs were independent influencing factors for DR.The older the onset of diabetes,the less likely to develop DR(OR:0.958,95%CI:0.942-0.975,P=0.000).Patients were then divided in terms of age at diabetes onset as follows:<50 y,50-59 y,60-69 y,and≥70 y.Compared with diabetes onset age<50 y,50-59 y(OR:0.463,95%CI:0.306-0.699,P=0.000),60-69 y(OR:0.329,95%CI:0.203-0.535,P=0.000)and≥70 y(OR:0.232,95%CI:0.094-0.577,P=0.002)were at a lower risk of DR.The prevalence of DR was highest in patients with diabetes onset age<50 y(29.5%,P<0.05).The Hb A1 c level(8.67±1.97)%and proportion of insulin injection(52.5%)in patients with diabetes onset<40 y were higher than in patients with older diabetes onset age(P<0.05).CONCLUSION:Diabetes onset at an earlier age and uncontrollable high Hb A1 c level could be independent risk factors for DR.展开更多
We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and c...We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.展开更多
With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate...With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.展开更多
基金funded by the National Basic Research Program of China (973 Program, 2014CB845700)the National Natural Science Foundation of China (Grant Nos. 11390371)Funding for the project has been provided by the National Development and Reform Commission
文摘The Large sky Area Multi-Object Fiber Spectroscopic Telescope（LAMOST） general survey is a spectroscopic survey that will eventually cover approximately half of the celestial sphere and collect 10 million spectra of stars, galaxies and QSOs. Objects in both the pilot survey and the first year regular survey are included in the LAMOST DR1. The pilot survey started in October 2011 and ended in June 2012, and the data have been released to the public as the LAMOST Pilot Data Release in August 2012. The regular survey started in September 2012, and completed its first year of operation in June 2013. The LAMOST DR1 includes a total of 1202 plates containing 2 955 336 spectra, of which 1 790 879 spectra have observed signalto-noise ratio（SNR） ≥ 10. All data with SNR ≥ 2 are formally released as LAMOST DR1 under the LAMOST data policy. This data release contains a total of 2 204 696 spectra, of which 1 944 329 are stellar spectra, 12 082 are galaxy spectra and 5017 are quasars. The DR1 not only includes spectra, but also three stellar catalogs with measured parameters： late A,FGK-type stars with high quality spectra（1 061 918 entries）, A-type stars（100 073 entries）, and M-type stars（121 522 entries）. This paper introduces the survey design, the observational and instrumental limitations, data reduction and analysis, and some caveats. A description of the FITS structure of spectral files and parameter catalogs is also provided.
基金Supported by the Health and Family Planning Commission Project from Jiangsu Province,China(No.H201672)Xuzhou Medical Innovation(Technical Breakthrough)Team from Xuzhou Health and Planning Committee(No.XWCX201610)。
文摘AIM:To assess the effect of age at diabetes onset and uncontrollable high Hb A1 c levels on the development of diabetic retinopathy(DR)among Chinese type 2 diabetes mellitus(DM)patients.METHODS:This was a cross-sectional survey of diabetic patients in Subei district,China.Data covering physical measurements,fasting blood-glucose(FBG),glycosylated hemoglobin(Hb A1 c),blood lipid,urinary albumin/creatinine ratio(UACR),ocular fundus examination,and diabetes treatment records were collected.An independent sample t-test were used to analyze differences.A Logistic regression analysis was applied to study the independent risk factors of DR.RESULTS:A total of 1282 patients with type 2 DM were enrolled,and 191 cases had DR(14.9%).The age at diabetes onset,education level,alcohol consumption,Hb A1 c level,UACR level,and hypoglycemic drugs were independent influencing factors for DR.The older the onset of diabetes,the less likely to develop DR(OR:0.958,95%CI:0.942-0.975,P=0.000).Patients were then divided in terms of age at diabetes onset as follows:<50 y,50-59 y,60-69 y,and≥70 y.Compared with diabetes onset age<50 y,50-59 y(OR:0.463,95%CI:0.306-0.699,P=0.000),60-69 y(OR:0.329,95%CI:0.203-0.535,P=0.000)and≥70 y(OR:0.232,95%CI:0.094-0.577,P=0.002)were at a lower risk of DR.The prevalence of DR was highest in patients with diabetes onset age<50 y(29.5%,P<0.05).The Hb A1 c level(8.67±1.97)%and proportion of insulin injection(52.5%)in patients with diabetes onset<40 y were higher than in patients with older diabetes onset age(P<0.05).CONCLUSION:Diabetes onset at an earlier age and uncontrollable high Hb A1 c level could be independent risk factors for DR.
基金supported by the National Natural Science Foundation of China(Grant No.92365206)the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)+1 种基金supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant No.92365206)+2 种基金the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.
