Muñoz Espinoza, Ariana

The basic entanglement swapping protocol allows to project two qubits, which have never interacted, onto a maximally entangled state. For deterministic swapping, the key ingredient is the maximal entanglement that was initially contained in two pairs of qubits and the capacity of projecting onto a Bell basis. Thus the basic and deterministic entanglement swapping scheme involves three maximal level of entanglement. In this work we propose probabilistic entanglement swapping processes performed with different amounts of initial entanglement. Besides that we suggest a non Bell measuring-basis, to introduce a third entanglement level in the process. Additionally, we propose the unambiguous state extraction scheme as the local mechanism for probabilistically achieving the EPR projection. The combination of these three elements allows us to design four strategies for performing probabilistic entanglement swapping. Surprisingly, we find a twofold entanglement threshold effect related to the concurrence of the measuring-basis. Specifically, the maximal probability of accomplishing a EPR projection becomes a constant for concurrences higher than or equal to threshold entanglement value. Thus, we show that maximal entanglement in the measuring-basis is not required for attaining the EPR projection. © 2023

In this work, four hundred and forty experimental solubility data points of 14 systems composed of methane and ionic liquids are considered to train a multilayer perceptron model. The main objective is to propose a simple procedure for the prediction of methane solubility in ionic liquids. Eight machine learning algorithms are tested to determine the appropriate model, and architectures composed of one input layer, two hidden layers, and one output layer are analyzed. The input variables of an artificial neural network are the experimental temperature (T) and pressure (P), the critical properties of temperature (Tc) and pressure (Pc), and the acentric (ω) and compressibility (Zc) factors. The findings show that a (4,4,4,1) architecture with the combination of T-P-Tc-Pc variables results in a simple 45-parameter model with an absolute prediction deviation of less than 12%. © 2024 by the authors.

In this work, 2099 experimental data of binary systems composed of CO2 and ionic liquids are studied to predict solubility using a multilayer perceptron. The dataset includes 33 different types of ionic liquids over a wide range of temperatures, pressures, and solubilities. The main objective of this work is to propose a procedure for the prediction of CO2 solubility in ionic liquids by establishing four stages to determine the model parameters: (1) selection of the learning algorithm, (2) optimization of the first hidden layer, (3) optimization of the second hidden layer, and (4) selection of the input combination. In this study, a bound is set on the number of model parameters: the number of model parameters must be less than the amount of predicted data. Eight different learning algorithms with (4,m,n,1)-type hidden two-layer architectures (m = 2, 4, …, 10 and n = 2, 3, …, 10) are studied, and the artificial neural network is trained with three input combinations with three combinations of thermodynamic variables such as temperature (T), pressure (P), critical temperature (Tc), critical pressure, the critical compressibility factor (Zc), and the acentric factor (ω). The results show that the 4-6-8-1 architecture with the input combination T-P-Tc-Pc and the Levenberg–Marquard learning algorithm is a very acceptable and simple model (95 parameters) with the best prediction and a maximum absolute deviation close to 10%. © 2022 by the authors.

In this work, a general thermodynamic consistency test is applied to analyze phase equilibrium data (PTx) for binary refrigerant and ionic liquid mixtures. The Valderrama-Patel-Teja (VPT) equation of state and the Kwak and Mansoori (KM) mixing rules are employed to correlate the solubility data of several refrigerants in different ionic liquids, and the Gibbs-Duhem equation is employed to check the thermodynamic consistency of six hundred forty-two experimental data points. The main purpose of this work is to analyze the influence of experimental data that are declared thermodynamically inconsistent on modeling the solubilities of refrigerants in ionic liquids using an artificial neural network. The results obtained via the test are classified into three categories: thermodynamically consistent, not fully consistent and thermodynamically inconsistent. Subsequently, a multilayer perceptron is trained to predict solubility in three cases: i) learning with isotherms that are declared thermodynamically consistent; ii) learning with isotherms, including those that are declared thermodynamically consistent and those that are not fully consistent; and iii) learning with all isotherms, even those that are declared thermodynamically inconsistent. For each case, the architecture, input combination and number of parameters necessary to achieve reasonable predictions are determined. The results show that the use of thermodynamically consistent and not fully consistent data is sufficient for finding an artificial neural network with a reasonable number of parameters.