Under neat conditions,an efficient method for synthesis of imidoesters has been developed using cyanatobenzenes and dicarbonyl compounds.Nucleophilic addition spontaneously occurred between the two kinds of materials ...Under neat conditions,an efficient method for synthesis of imidoesters has been developed using cyanatobenzenes and dicarbonyl compounds.Nucleophilic addition spontaneously occurred between the two kinds of materials at room temperature with yields of up to 90%.A mechanism directed towards to the imidoester formation has been proposed.展开更多
Quantum process tomography is often used to completely characterize an unknown quantum process.However, it may lead to an unphysical process matrix, which will cause the loss of information with respect to the tomogra...Quantum process tomography is often used to completely characterize an unknown quantum process.However, it may lead to an unphysical process matrix, which will cause the loss of information with respect to the tomography result. Convex optimization, widely used in machine learning, is able to generate a global optimum that best fits the raw data while keeping the process tomography in a legitimate region. Only by correctly revealing the original action of the process can we seek deeper into its properties like its phase transition and its Hamiltonian. Here, we reconstruct the seawater channel using convex optimization and further test it on the seven fundamental gates. We compare our method to the standard-inversion and norm-optimization approaches using the cost function value and our proposed state deviation. The advantages convince that our method enables a more precise and robust estimation of the elements of the process matrix with less demands on preliminary resources. In addition, we examine on a set of non-unitary channels and the reconstructions reach up to 99:5% accuracy. Our method offers a more universal tool for further analyses on the components of the quantum channels and we believe that the crossover between quantum process tomography and convex optimization may help us move forward to machine learning of quantum channels.展开更多
基金the National Natural Science Foundation of China (Nos.21372265 and 61271059)the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry,the Natural Science Foundation Project of CQ CSTC (Nos.CSTC2013jcyjA0217 and CSTC2010BB4086)the Fundamental Research Funds for the Central Universities(Nos.CQDXWL2013-Z012 and CDJZR-14225502)
文摘Under neat conditions,an efficient method for synthesis of imidoesters has been developed using cyanatobenzenes and dicarbonyl compounds.Nucleophilic addition spontaneously occurred between the two kinds of materials at room temperature with yields of up to 90%.A mechanism directed towards to the imidoester formation has been proposed.
基金supported by the National Key R&D Program of China(2019YFA0308700,2017YFA0303700)the National Natural Science Foundation of China(61734005,11761141014,and 11690033)+2 种基金the Science and Technology Commission of Shanghai Municipality(15QA1402200,16JC1400405,and 17JC1400403)the Shanghai Municipal Education Commission(16SG09 and 2017-01-07-00-02-E00049)additional support from a Shanghai Talent Program.
文摘Quantum process tomography is often used to completely characterize an unknown quantum process.However, it may lead to an unphysical process matrix, which will cause the loss of information with respect to the tomography result. Convex optimization, widely used in machine learning, is able to generate a global optimum that best fits the raw data while keeping the process tomography in a legitimate region. Only by correctly revealing the original action of the process can we seek deeper into its properties like its phase transition and its Hamiltonian. Here, we reconstruct the seawater channel using convex optimization and further test it on the seven fundamental gates. We compare our method to the standard-inversion and norm-optimization approaches using the cost function value and our proposed state deviation. The advantages convince that our method enables a more precise and robust estimation of the elements of the process matrix with less demands on preliminary resources. In addition, we examine on a set of non-unitary channels and the reconstructions reach up to 99:5% accuracy. Our method offers a more universal tool for further analyses on the components of the quantum channels and we believe that the crossover between quantum process tomography and convex optimization may help us move forward to machine learning of quantum channels.
