Related papers: Numerically efficient density-matrix technique for…
This paper presents a new approach of applying partial least squares method combined with a physical principle based dominant factor. The characteristic line intensity of the specific element was taken to build up the dominant factor to…
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and…
Gain in current-driven semiconductor heterostructure devices is calculated within the theory of nonequilibrium Green functions. In order to treat the nonequilibrium distribution self-consistently the full two-time structure of the theory is…
A recently proposed Markov approach provides Lindblad-type scattering superoperators, which ensure the physical (positive-definite) character of the many-body density matrix. We apply the mean-field approximation to such many-body equation,…
We propose to use ultracold fermionic atoms in one-dimensional optical lattices to quantum simulate the electronic transport in quantum cascade laser (QCL) structures. The competition between the coherent tunneling among (and within) the…
The link between the inversion gain of quantum cascade structures and the Bloch gain in periodic superlattices is presented. The proposed theoretical model based on the density matrix formalism is able to treat the gain mechanism of the…
With the rapid development of science and technology, the problem of energy load monitoring and decomposition of electrical equipment has been receiving widespread attention from academia and industry. For the purpose of improving the…
Expert systems often operate in domains characterized by class-imbalanced tabular data, where detecting rare but critical instances is essential for safety and reliability. While conventional approaches, such as cost-sensitive learning,…
Silicon-based quantum cascade lasers (QCLs) offer the prospect of integrating coherent THz radiation sources with silicon microelectronics. Theoretical studies have proposed a variety of n-type SiGe-based heterostructures as design…
A multivariate dominant factor based non-linearized PLS model is proposed. The intensities of different lines were taken to construct a multivariate dominant factor model, which describes the dominant concentration information of the…
Quantum cascade lasers are unipolar semiconductor lasers covering a wide range of the infrared and terahertz spectrum. Lasing action is achieved by using optical intersubband transitions between quantized states in specifically designed…
Quantum cascade lasers (QCLs) are becoming a key tool for plenty of applications, from the mid-infrared (mid-IR) to the THz range. Progress in related areas, such as the development of ultra-low-loss crystalline microresonators, optical…
The successful quasi-particle model is compared with recent lattice data of the coefficients in the Taylor series expansion of the excess pressure at finite temperature and baryon density. A chain of approximations, starting from QCD to…
The manufacturing cost of quantum cascade lasers is still a major bottleneck for the adoption of this technology for chemical sensing. The integration of Mid-Infrared sources on Si substrate based on CMOS technology paves the way for…
We present a novel scheme for hybrid integration of quantum cascade laser bars with germanium-on-silicon waveguides operating in the mid-infrared. The laser bars are flip-chip bonded onto a germanium-on-silicon target chip without active…
Non-Markovian quantum state diffusion (NMQSD) is an exact method for calculating the reduced density matrix of an arbitrary subsystem interacting linearly with the radiation field. Applications of the theory have however been few due to the…
Density matrices are powerful mathematical tools for the description of closed and open quantum systems. Recently, methods for the direct computation of density matrix elements in scalar quantum field theory were developed based on thermo…
Density estimation is a central task in statistics and machine learning. This problem aims to determine the underlying probability density function that best aligns with an observed data set. Some of its applications include statistical…
In the thesis we present an analytic approach towards exact description for steady state density operators of nonequilibrium quantum dynamics in the framework of open systems. We employ the so-called quantum Markovian semi-group evolution,…
Intrinsically low lattice thermal conductivity ($\kappa_l$) is a desired requirement in many crystalline solids such as thermal barrier coatings and thermoelectrics. Here, we design an advanced machine-learning (ML) model based on crystal…