Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reac...Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor model.This tensor model is an extension of the collaborative filtering algorithm used for nuclear data.It is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training data.The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases.Predictions were made for long-lived fission products such as^(60)Co,^(79)Se,^(93)Zr,^(107)P,^(126)Sn,and^(137)Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes.This method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste.展开更多
Quantum multi-parameter estimation has recently attracted increased attention due to its wide applications, with a primary goal of designing high-precision measurement schemes for unknown parameters. While existing re...Quantum multi-parameter estimation has recently attracted increased attention due to its wide applications, with a primary goal of designing high-precision measurement schemes for unknown parameters. While existing research has predominantly concentrated on time-independent Hamiltonians, little has been known about quantum multi-parameter estimation for time-dependent Hamiltonians due to the complexity of quantum dynamics. This work bridges the gap by investigating the precision limit of multi-parameter quantum estimation for a qubit in an oscillating magnetic field model with multiple unknown frequencies. As the well-known quantum Cramer–Rao bound is generally unattainable due to the potential incompatibility between the optimal measurements for different parameters, we use the most informative bound instead which is always attainable and equivalent to the Holevo bound in the asymptotic limit. Moreover, we apply additional Hamiltonian to the system to engineer the dynamics of the qubit. By utilizing the quasi-Newton method, we explore the optimal schemes to attain the highest precision for the unknown frequencies of the magnetic field, including the simultaneous optimization of initial state preparation, the control Hamiltonian and the final measurement. The results indicate that the optimization can yield much higher precisions for the field frequencies than those without the optimizations. Finally,we study the robustness of the optimal control scheme with respect to the fluctuation of the interested frequencies, and the optimized scheme exhibits superior robustness to the scenario without any optimization.展开更多
Interpreting reservoir properties through log data and logging responses in complex strata is critical for efficient petroleum exploitation,particularly for metamorphic rocks.However,the unsatisfactory accuracy of suc...Interpreting reservoir properties through log data and logging responses in complex strata is critical for efficient petroleum exploitation,particularly for metamorphic rocks.However,the unsatisfactory accuracy of such interpretations in complex reservoirs has hindered their widespread application,resulting in severe inconvenience.In this study,we proposed a multi-mineral model based on the least-square method and an optimal principle to interpret the logging responses and petrophysical properties of complex hydrocarbon reservoirs.We began by selecting the main minerals based on a comprehensive analysis of log data,X-ray diffraction,petrographic thin sections and scanning electron microscopy(SEM)for three wells in the Bozhong 19-6 structural zone.In combination of the physical properties of these minerals with logging responses,we constructed the multi-mineral model,which can predict the log curves,petrophysical properties and mineral profile.The predicted and measured log data are evaluated using a weighted average error,which shows that the multi-mineral model has satisfactory prediction performance with errors below 11%in most intervals.Finally,we apply the model to a new well“x”in the Bozhong 19-6 structural zone,and the predicted logging responses match well with measured data with the weighted average error below 11.8%for most intervals.Moreover,the lithology is dominated by plagioclase,K-feldspar,and quartz as shown by the mineral profile,which correlates with the lithology of the Archean metamorphic rocks in this region.It is concluded that the multi-mineral model presented in this study provides reasonable methods for interpreting log data in complex metamorphic hydrocarbon reservoirs and could assist in efficient development in the future.展开更多
基金supported by the Key Laboratory of Nuclear Data foundation(No.JCKY2022201C157)。
文摘Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor model.This tensor model is an extension of the collaborative filtering algorithm used for nuclear data.It is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training data.The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases.Predictions were made for long-lived fission products such as^(60)Co,^(79)Se,^(93)Zr,^(107)P,^(126)Sn,and^(137)Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes.This method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste.
基金Project supported by the National Natural Science Foundation of China (Grant No. 12075323)。
文摘Quantum multi-parameter estimation has recently attracted increased attention due to its wide applications, with a primary goal of designing high-precision measurement schemes for unknown parameters. While existing research has predominantly concentrated on time-independent Hamiltonians, little has been known about quantum multi-parameter estimation for time-dependent Hamiltonians due to the complexity of quantum dynamics. This work bridges the gap by investigating the precision limit of multi-parameter quantum estimation for a qubit in an oscillating magnetic field model with multiple unknown frequencies. As the well-known quantum Cramer–Rao bound is generally unattainable due to the potential incompatibility between the optimal measurements for different parameters, we use the most informative bound instead which is always attainable and equivalent to the Holevo bound in the asymptotic limit. Moreover, we apply additional Hamiltonian to the system to engineer the dynamics of the qubit. By utilizing the quasi-Newton method, we explore the optimal schemes to attain the highest precision for the unknown frequencies of the magnetic field, including the simultaneous optimization of initial state preparation, the control Hamiltonian and the final measurement. The results indicate that the optimization can yield much higher precisions for the field frequencies than those without the optimizations. Finally,we study the robustness of the optimal control scheme with respect to the fluctuation of the interested frequencies, and the optimized scheme exhibits superior robustness to the scenario without any optimization.
基金funded by Science and Technology Major Project of China National Offshore Oil Corporation(CNOOC-KJ 135 ZDXM36 TJ 08TJ).
文摘Interpreting reservoir properties through log data and logging responses in complex strata is critical for efficient petroleum exploitation,particularly for metamorphic rocks.However,the unsatisfactory accuracy of such interpretations in complex reservoirs has hindered their widespread application,resulting in severe inconvenience.In this study,we proposed a multi-mineral model based on the least-square method and an optimal principle to interpret the logging responses and petrophysical properties of complex hydrocarbon reservoirs.We began by selecting the main minerals based on a comprehensive analysis of log data,X-ray diffraction,petrographic thin sections and scanning electron microscopy(SEM)for three wells in the Bozhong 19-6 structural zone.In combination of the physical properties of these minerals with logging responses,we constructed the multi-mineral model,which can predict the log curves,petrophysical properties and mineral profile.The predicted and measured log data are evaluated using a weighted average error,which shows that the multi-mineral model has satisfactory prediction performance with errors below 11%in most intervals.Finally,we apply the model to a new well“x”in the Bozhong 19-6 structural zone,and the predicted logging responses match well with measured data with the weighted average error below 11.8%for most intervals.Moreover,the lithology is dominated by plagioclase,K-feldspar,and quartz as shown by the mineral profile,which correlates with the lithology of the Archean metamorphic rocks in this region.It is concluded that the multi-mineral model presented in this study provides reasonable methods for interpreting log data in complex metamorphic hydrocarbon reservoirs and could assist in efficient development in the future.