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Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in high energy physics by offering computational speedups. In this study, we employ a support vector machine with a quantum kernel estimator (QSVM-Kernel method) to a recent LHC flagship physics analysis: tt¯H (Higgs boson production in association with a top quark pair). In our quantum simulation study using up to 20 qubits and up to 50000 events, the QSVM-Kernel method performs as well as its classical counterparts in three different platforms from Google Tensorflow Quantum, IBM Quantum, and Amazon Braket. Additionally, using 15 qubits and 100 events, the application of the QSVM-Kernel method on the IBM superconducting quantum hardware approaches the performance of a noiseless quantum simulator. Our study confirms that the QSVM-Kernel method can use the large dimensionality of the quantum Hilbert space to replace the classical feature space in realistic physics data sets. Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In this study, we employ a support vector machine with a quantum kernel estimator (QSVM-Kernel method) to a recent LHC flagship physics analysis: $t\bar{t}H$ (Higgs boson production in association with a top quark pair). In our quantum simulation study using up to 20 qubits and up to 50000 events, the QSVM-Kernel method performs as well as its classical counterparts in three different platforms from Google Tensorflow Quantum, IBM Quantum and Amazon Braket. Additionally, using 15 qubits and 100 events, the application of the QSVM-Kernel method on the IBM superconducting quantum hardware approaches the performance of a noiseless quantum simulator. Our study confirms that the QSVM-Kernel method can use the large dimensionality of the quantum Hilbert space to replace the classical feature space in realistic physics datasets.

To utilize incinerator fly ashes (IFAs) as cement raw materials, the knowledge of their total Cl content is indispensible, hence a process which can digest IFA samples completely becomes critical. This study describes a two-stage microwave-assisted digestion process in which various acids and a set of terminal temperatures of 180–210 °C are applied. In the first stage, three types of acids are utilized, namely, concentrated HNO 3 , concentrated HF, and a mixture of concentrated HNO 3 and HF. In the second stage, saturated H 3 BO 3 is added to the cooled sample residues, and the temperature programming applied is the same as that of the first stage. Results indicate that a complete digestion can only be achieved if the two-stage digestion process is operated at a terminal temperature of 210 °C, coupled with a mixture of concentrated HNO 3 and HF. Although the microwave-assisted digestion process operated at 180 °C is sanctioned by both USA and Taiwan environmental authorities, IFA samples treated accordingly often leave a considerable amount of undissolved residues.

Summary A polynomial functional relationship with errors in both variables can be consistently estimated by constructing an ordinary least squares estimator for the regression coefficients, assuming hypothetically the latent true regressor variable to be known, and then adjusting for the errors. If normality of the error variables can be assumed, the estimator can be simplified considerably. Only the variance of the errors in the regressor variable and its covariance with the errors of the response variable need to be known. If the variance of the errors in the dependent variable is also known, another estimator can be constructed.