John Henry Reina

Quantum batteries (QBs) exploit collective quantum resources to surpass the limits of classical energy storage and power delivery. We analyze N -qubit cavity-coupled QBs governed by Dicke and Tavis-Cummings models under Gaussian driving and open-system dynamics. Finite-size scaling laws O ( N ) ∼ N α demonstrate an optimal region of relaxation and dephasing where coherent driving stabilizes entanglement entropy growth for thermodynamic observables (maximum energy E max , charging time τ , and maximum power P ¯ max ) and for qubit and cavity entanglement entropies. The Dicke model exhibits entropy-suppressed extensive behavior, while the Tavis-Cummings model achieves superextensive scaling with α E max ∈ [ 1.08 , 1.26 ] , α τ ≈ − 0.49 , and α P ¯ max ∈ [ 1.57 , 1.73 ] , supported by qubit-cavity entanglement. We demonstrate that dissipation can act as a stabilizer source, yielding scaling benchmarks that are relevant to several experimental platforms. Our findings connect entanglement, dissipation-enhanced scaling laws, and superabsorption, outlining a pathway toward scalable quantum batteries offering practical quantum advantage.

We develop machine learning models for the automated characterization of quantum noise spectroscopy for non-Hermitian two-level systems. We use the Random Forest, Support Vector, and Feedforward Neural Network regression algorithms to perform a highly accurate regression of the two-level system-bath coupling strength. High accuracy Ohmicity classification was implemented to provide a complete characterization of the spectral density function. We define a time-averaged trace-distance metric to feed the machine learning algorithms, which, together with numerically exact populations as inputs, produce a highly accurate non-Markovian regression spanning the transition from fast to slow baths and from weak to strong coupling regimes of the interaction. The dynamics database of the non-Hermitian systems has been built up within the independent spin-boson and pure dephasing model.

This chapter focuses on studying urban pollutants in Colombia's most populated cities by developing local instrumentation and implementing chemical transport models (CTMs). The instrumentation includes Scanning Depolarization Lidar System, MAX-DOAS and low-cost in situ devices. Additionally, particulate matter is collected for chemical and morphological analysis using different techniques to identify the main sources of emissions. The chapter aims to provide examples of the findings from a study that is set to establish a foundation for adapting chemical and transport models to complex topography, such as coupling WRF-Chem with LOTOS-EUROS and setting the basis for incorporating data assimilation techniques to enhance the models' predictive capabilities in tropical conditions.

Employing currently available quantum technology, we design and implement a nonclassically correlated SWAP heat engine that allows to achieve an efficiency above the standard Carnot limit. Such an engine also boosts the amount of extractable work, in a wider parameter window, with respect to engine's cycle in the absence of initial quantum correlations in the working substance. The boosted efficiency arises from a trade-off between the entropy production and the consumption of quantum correlations during the full thermodynamic cycle. We derive a generalized second-law limit for the correlated cycle and implement a proof-of-principle demonstration of the engine efficiency enhancement by effectively tailoring the thermal engine on a cloud quantum processor.

Employing currently available quantum technology, we design and implement a non-classically correlated SWAP heat engine that allows to achieve an efficiency above the standard Carnot limit. Such an engine also boosts the amount of extractable work, in a wider parameter window, with respect to engine's cycle in the absence of initial quantum correlations in the working substance. The boosted efficiency arises from a trade-off between the entropy production and the consumption of quantum correlations during the full thermodynamic cycle. We derive a generalized second-law limit for the correlated cycle and implement a proof-of-principle demonstration of the engine efficiency enhancement by effectively tailoring the thermal engine on a cloud quantum processor.