提出一种以CaCl_(2)为氯化剂,采用氯化焙烧法从铜熔炼渣中高效回收锌的工艺。利用热力学计算、热重–差热(TG-DSC)分析和X射线衍射(XRD)等手段,研究氯化反应机理和氯化焙烧过程动力学。结果表明,CaCl_(2)氧化分解和所有含锌相的氯化反...提出一种以CaCl_(2)为氯化剂,采用氯化焙烧法从铜熔炼渣中高效回收锌的工艺。利用热力学计算、热重–差热(TG-DSC)分析和X射线衍射(XRD)等手段,研究氯化反应机理和氯化焙烧过程动力学。结果表明,CaCl_(2)氧化分解和所有含锌相的氯化反应温度均分别高于774.3和825℃。铜熔炼渣的氯化焙烧过程可分为4个阶段,依次为吸附水脱除、结晶水脱除、含铁相氧化和锌的氯化挥发。铁氧化阶段和锌氯化挥发阶段的表观活化能分别为101.70和84.4 k J/mol,铁氧化过程的最概然机理函数为Avrami–Erofeev方程(n=2),锌氯化过程符合未反应核收缩模型且受化学反应控制。展开更多
Quantum computers promise to solve finite-temperature properties of quantum many-body systems,which is generally challenging for classical computers due to high computational complexities.Here,we report experimental p...Quantum computers promise to solve finite-temperature properties of quantum many-body systems,which is generally challenging for classical computers due to high computational complexities.Here,we report experimental preparations of Gibbs states and excited states of Heisenberg X X and X X Z models by using a 5-qubit programmable superconducting processor.In the experiments,we apply a hybrid quantum–classical algorithm to generate finite temperature states with classical probability models and variational quantum circuits.We reveal that the Hamiltonians can be fully diagonalized with optimized quantum circuits,which enable us to prepare excited states at arbitrary energy density.We demonstrate that the approach has a self-verifying feature and can estimate fundamental thermal observables with a small statistical error.Based on numerical results,we further show that the time complexity of our approach scales polynomially in the number of qubits,revealing its potential in solving large-scale problems.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 52074363, 52104355, 51922108, U20A20273)the National Key R&D Program of China (No. 2019YFC1907402)。
基金the financial supports from the National Natural Science Foundation of China(No.51902239)the Natural Science Foundation of Shaanxi Province,China(No.2020JQ-808)the National Innovation and Entrepreneurship Training Program for College Students,China(No.202110702040)。
文摘提出一种以CaCl_(2)为氯化剂,采用氯化焙烧法从铜熔炼渣中高效回收锌的工艺。利用热力学计算、热重–差热(TG-DSC)分析和X射线衍射(XRD)等手段,研究氯化反应机理和氯化焙烧过程动力学。结果表明,CaCl_(2)氧化分解和所有含锌相的氯化反应温度均分别高于774.3和825℃。铜熔炼渣的氯化焙烧过程可分为4个阶段,依次为吸附水脱除、结晶水脱除、含铁相氧化和锌的氯化挥发。铁氧化阶段和锌氯化挥发阶段的表观活化能分别为101.70和84.4 k J/mol,铁氧化过程的最概然机理函数为Avrami–Erofeev方程(n=2),锌氯化过程符合未反应核收缩模型且受化学反应控制。
基金Project supported by the State Key Development Program for Basic Research of China(Grant No.2017YFA0304300)the National Natural Science Foundation of China(Grant Nos.11934018,11747601,and 11975294)+4 种基金Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB28000000)Scientific Instrument Developing Project of Chinese Academy of Sciences(Grant No.YJKYYQ20200041)Beijing Natural Science Foundation(Grant No.Z200009)the Key-Area Research and Development Program of Guangdong Province,China(Grant No.2020B0303030001)Chinese Academy of Sciences(Grant No.QYZDB-SSW-SYS032)。
文摘Quantum computers promise to solve finite-temperature properties of quantum many-body systems,which is generally challenging for classical computers due to high computational complexities.Here,we report experimental preparations of Gibbs states and excited states of Heisenberg X X and X X Z models by using a 5-qubit programmable superconducting processor.In the experiments,we apply a hybrid quantum–classical algorithm to generate finite temperature states with classical probability models and variational quantum circuits.We reveal that the Hamiltonians can be fully diagonalized with optimized quantum circuits,which enable us to prepare excited states at arbitrary energy density.We demonstrate that the approach has a self-verifying feature and can estimate fundamental thermal observables with a small statistical error.Based on numerical results,we further show that the time complexity of our approach scales polynomially in the number of qubits,revealing its potential in solving large-scale problems.
基金financially supported by the National Natural Science Foundation of China(Nos.52074363,52104355,51922108,U20A20273)the National Key Research and Development Program of China(Nos.2019YFC1907402,2018YFC1902501)。
基金the National Natural Science Foundation of China(No.52004342)Innovation-driven Project of Central South University,China(No.150240015)Natural Science Fund for Outstanding Young Scholar of Hunan Province,China(No.2021JJ20065).
基金the financial supports from the National Natural Science Foundation of China(No.U20A20273)the National Key R&D Program of China(No.2019YFC1907400)+1 种基金the Science and Technology Innovation Program of Hunan Province,China(No.2021RC3005)the Natural Science Fund for Distinguished Young Scholar of Hunan Province,China(No.2022JJ10078)。
基金the financial supports from the National Natural Science Foundation of China(Nos.51904351,U20A20273)the National Key R&D Program of China(No.2019YFC1907400)+1 种基金the Science and Technology Innovation Program of Hunan Province,China(No.2021RC3005)the Innovation Driven Project of Central South University,China(No.2020CX028)。
基金the financial supports from the National Natural Science Foundation of China(Nos.51904351,51620105013,U20A20273)the National Key R&D Program of China(No.2019YFC1907400)+2 种基金the Science and Technology Innovation Program of Hunan Province,China(No.2021RC3005)the Major Technological Innovation Projects of Shandong Province,China(No.2019JZZY010404)the Innovation Driven Program of Central South University,China(No.2020CX028)。
基金supported by the National Natural Science Foundation of China(No.U20A20273)the Natural Science Foundation for Distinguished Young Scholars of Hunan Province,China(No.2022JJ10078)the Innovation Driven Project of Central South University,China(No.2020CX028).
