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Roberto Bernal-Jaquez

Publications and source records attributed to Roberto Bernal-Jaquez.

3 recordsLinked to original sources

Exact Gaussian Entanglement Dynamics and Initial-State Control in Coupled Parametric Oscillators

We study two bilinearly coupled harmonic oscillators driven by a common time-dependent spring. A fixed normal-mode transformation reduces the pair to two parametric oscillators, each solved exactly by its Lewis-Riesenfeld invariant and Ermakov-Pinney amplitude, including the ladder-operator construction and the Lewis-Riesenfeld phase. For invariant-vacuum inputs the physical two-mode covariance matrix is obtained in closed form, and the logarithmic negativity collapses to $E_{N}=\frac{1}{2} \operatorname{arcosh}(X/2)$, where a single dimensionless invariant $X \geq 2$ measures the relative phase-space deformation of the two normal modes. We give a normalization-explicit account of the Duan--Simon comparison: after local symplectic optimization the Duan test and the positive partial transpose share the entanglement threshold for this pure exchange-symmetric Gaussian family, but the optimized Duan quantity is a witness, not an independent measure, with $D_{\rm opt}/D_{\rm sep}=e^{-E_N}$. We carefully separate a genuinely separable product preparation from the already-entangled ground state of the coupled Hamiltonian, and use only the former for claims of entanglement generation. Under sinusoidal stiffness modulation a separable input shows bounded correlations away from resonance and sustained growth near parametric resonance, and we demonstrate numerically that the initial width and chirp act as two independent, experimentally accessible controls of the entanglement at a chosen target time. An $SU(1,1)$ formulation identifies $2 E_{N}$ with the hyperbolic separation of the two normal-mode squeezing trajectories on the $SU(1,1)/U(1)$ disk and shows that entanglement is governed by both the relative squeezing magnitude and the relative squeezing angle; ; we exhibit regimes in which nearly equal magnitudes still produce strong entanglement through the angle alone.

quant-ph↗

Obtaining transferable chemical insight from solving machine-learning classification problems: Thermodynamical properties prediction, atomic composition as good as Coulomb matrix

Machine learning (ML) can be used to construct surrogate models for the fast prediction of a property of interest. ML can thus be applied to chemical projects, where the usual experimentation or calculation techniques can take hours or days for just one sample. In this manner, the most promising candidate samples could be extracted from an extensive database and subjected to further in-depth analysis. Despite their broad applicability, it can be challenging to apply ML methods to a given chemical problem since a multitude of design decisions must be made, such as the molecular descriptor to use or the optimizer to train the model. Here we present a methodology for the meaningful exploration of a given molecular problem through classification experiments. This conceptually simple methodology results in transferable insight on the selected problem and can be used as a platform from which prediction difficulty is estimated, molecular representations are tested and refined, and more precise or ambitious projects can be undertaken. Physicochemical insight can also be obtained. This methodology is illustrated through the use of multiple molecular descriptors for the prediction of enthalpy, Gibbs' free energy, zero-point vibrational energy, and constant-volume calorific capacity of the molecules from the public database QM9 [Ramakrishnan2014] with 133,885 organic molecules. A noteworthy result is that for the classification problem we propose, the low-resolution descriptor `atomic composition' [Tchagang2019] can reach a classification rate almost on par with the high-resolution `sorted Coulomb matrix' [Rupp2012,Montavon2012,Hansen2013] ($>90\%$), provided that an appropriate optimizer is used during training.

physics.chem-ph↗

Virus and Warning Spread in Dynamical Networks

Recent work on information survival in sensor and human P2P networks, try to study the datum preservation or the virus spreading in a network under the dynamical system approach. Some interesting solutions propose to use non-linear dynamical systems and fixed point stability theorems, providing closed form formulas that depend on the largest eigenvalue of the dynamic system matrix. Given that in a the Web there can be messages from one place to another, and that this messages can be, with some probability, new unclassified virus warning messages as well as worms or other kind of virus, the sites can be infected very fast. The question to answer is how and when a network infection can become global and how it can be controlled or at least how to stabilize his spreading in such a way that it becomes confined below a fixed portion of the network. In this paper, we try to make a step ahead in this direction and apply classic results of the dynamical systems theory to model the behaviour of a network where warning messages and virus spread.

physics.soc-ph↗