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Dennis Lima

Publications and source records attributed to Dennis Lima.

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Before We Inject: Assessing the Impact of Silica-Based Aerosols on Stratospheric Chemistry via a Kinetic Model Informed by Molecular Dynamics

Stratospheric aerosol injection (SAI) has been proposed as a geoengineering strategy to mitigate global warming by increasing Earth's albedo. Silica-based materials, such as diamond-doped silica aerogels, have shown promising optical properties, but their impact on stratospheric chemistry, ozone one in particular, remains largely unknown. Here, we present first-principles molecular dynamics (MD) simulations of the heterogeneous reaction between hydrogen chloride ($\mathrm{HCl}$) and chlorine nitrate ($\mathrm{ClONO_2}$), two main reservoirs of stratospheric chlorine and nitrogen species, on a dry, hydroxylated $\alpha$-quartz silica interface. Our results reveal a barrierless reaction pathway toward the formation of chlorine gas ($\mathrm{Cl}_2$), a major contributor to stratospheric ozone loss. We design a heterogeneous kinetic model informed by our MD simulation and available experimental data: despite the barrierless formation of $\mathrm{Cl_2}$, the higher surface affinities and partial pressures of $\mathrm{HNO_3}$ and $\mathrm{HCl}$ compared to those of $\mathrm{ClONO_2}$ result in a negligible reaction probability, $\gamma_\mathrm{ClONO_2}$, upon chlorine nitrate collision with the silica surface. Since $\gamma_\mathrm{ClONO_2}$ enters as a proportionality constant in the definition of the heterogeneous reaction rate, our kinetic model indicates that the injection of silica-based aerosols may have only a limited impact on stratospheric ozone depletion driven by $\mathrm{HCl}$ and $\mathrm{ClONO_2}$ chemistry. At the same time, our findings also underscore the scarcity of experimental data, the need of better theoretical frameworks for the inclusion of MD results into kinetic models, and the urgency for further experimental validations of silica-based SAI technologies before their deployment in climate intervention strategies.

physics.chem-ph

Advances in Quantum Genetic Algorithms

Quantum Genetic Algorithms (QGAs) are an emerging field of multivariate quantum optimization that emulate Darwinian evolution and natural selection, with vast applications in chemistry and engineering. The appropriate application of fitness functions and fitness selection are the problem-encoding step and the slowest step in designing QGAs for specific physical applications. In this paper, we provide a comprehensive review of these crucial steps. Our survey maps cases of quantum advantage, classifies and illustrates QGAs and their subroutines, and discusses the two main physical problems tackled by QGAs: potential energy minimization of particles on a sphere, and molecular eigensolving. We conclude that the encoding used by the Thomson problem is a decisive step toward the use of QGAs in a variety of physical applications, while Grover's search as a selection step in Reduced QGAs is the main driver of quantum speedup.

quant-ph

Exponential speed-up in VQE molecular energy ranking with Sridhara-compressed Hamiltonians

Polycyclic aromatic hydrocarbons (PAHs) are residual and intermediary molecules in the Chemical Vapor Deposition (CVD) to produce graphene from methane. Ranking a combinatorial space of variants of PAHs by energy allows the CVD to be optimized, while simulations of PAHs are strong candidates for quantum advantage in quantum computers. We extend on Sridhara's root formula to perform block diagonalization (SBD) of six PAHs using Hartree-Fock Hamiltonians with STO-3G basis set and $(2,2)$, $(4,4)$, $(6,6)$ settings of active orbitals and active electrons. We show that the proposed SBD algorithm followed by Variational Quantum Eigensolver (VQE) allows ranking molecules by ground state energy with $77.8\%$ of success in comparison with the uncompressed VQE, while speeding up the VQE simulation in $164.16\%$ (median) keeping its average error of active space reduction down to $0.09\%$. We conclude that the flexibilization of constraints of the SBD algorithm makes it a fast and reliable estimator for active space reduction in molecular simulation.

quant-ph

Unitarization of Pseudo-Unitary Quantum Circuits in the S-matrix Framework

Pseudo-unitary circuits are recurring in both S-matrix theory and analysis of No-Go theorems. We propose a matrix and diagrammatic representation for the operation that maps S-matrices to T-matrices and, consequently, a unitary group to a pseudo-unitary one. We call this operation ``partial inversion'' and show its diagrammatic representation in terms of permutations. We find the expressions for the deformed metrics and deformed dot products that preserve physical constraints after partial inversion. Subsequently, we define a special set that allows for the simplification of expressions containing infinities in matrix inversion. Finally, we propose a renormalized-growth algorithm for the T-matrix as a possible application. The outcomes of our study expand the methodological toolbox needed to build a family of pseudo-unitary and inter-pseudo-unitary circuits with full diagrammatic representation in three dimensions, so that they can be used to exploit pseudo-unitary flexibilization of unitary No-Go Theorems and renormalized circuits of large scattering lattices.

hep-th