We have applied a machine learning algorithm to predict the emergence of environment-induced spontaneous synchronization between two qubits in an open system setting. In particular, we have considered three different models, encompassing global and local dissipation regimes, to describe the open system dynamics of the qubits. We have utilized the $k$-nearest-neighbor algorithm to estimate the long-time synchronization behavior of the qubits only using the early time expectation values of qubit observables in these three distinct models. Our findings clearly demonstrate the possibility of determining the occurrence of different synchronization phenomena with high precision even at the early stages of the dynamics using a machine learning-based approach. Moreover, we show the robustness of our approach against potential measurement errors in experiments by considering random errors in the qubit expectation values, initialization errors, as well as deviations in the environment temperature. We believe that the presented results can prove to be useful in experimental studies on the determination of quantum synchronization.

We study the exact open system dynamics of one- and two-qubit gates during a measurement-based quantum computation considering non-Markovian environments. We obtain analytical solutions for the average gate fidelities and analyze them for amplitude damping and phase damping channels. We show, for both channels, that the average fidelity is identical for the $X$ gate and $Z$ gate and very similar for the $\pi /4$ gate when considering the amplitude damping channel. Also, we show that fast application of the projective measurements does not necessarily imply high gate fidelity nor does slow application necessarily imply low gate fidelity. Indeed, for highly non-Markovian environments, it is of utmost importance to know the best time to perform the measurements, since a huge variation in the gate fidelity may occur given this scenario. Furthermore, we show that whereas for amplitude damping the knowledge of the dissipative map is sufficient to determine the best measurement times, i.e., the best times at which measures are taken, the same is not necessarily true for phase damping. For the latter, the time of the set of measures becomes crucial since a phase error in one qubit can fix the phase error that takes place in another. Finally, we show that these peculiar results disappear if all qubits are subjected to Markovian processes.

In recent years, Generative Artificial Intelligence (GenAI) has undergone a profound transformation in addressing intricate tasks involving diverse modalities such as textual, auditory, visual, and pictorial generation. Within this spectrum, text-to-image (TTI) models have emerged as a formidable approach to generating varied and aesthetically appealing compositions, spanning applications from artistic creation to realistic facial synthesis, and demonstrating significant advancements in computer vision, image processing, and multimodal tasks. The advent of Latent Diffusion Models (LDMs) signifies a paradigm shift in the domain of AI capabilities. This article delves into the feasibility of employing the Stable Diffusion LDM to illustrate literary works. For this exploration, seven classic Brazilian books have been selected as case studies. The objective is to ascertain the practicality of this endeavor and to evaluate the potential of Stable Diffusion in producing illustrations that augment and enrich the reader's experience. We will outline the beneficial aspects, such as the capacity to generate distinctive and contextually pertinent images, as well as the drawbacks, including any shortcomings in faithfully capturing the essence of intricate literary depictions. Through this study, we aim to provide a comprehensive assessment of the viability and efficacy of utilizing AI-generated illustrations in literary contexts, elucidating both the prospects and challenges encountered in this pioneering application of technology.

This study proposes a multi-layered framework to quantify gender and ethnic bias in state-of-the-art text-to-image models using geographically grounded evaluations across ten American countries. We evaluate Stable Diffusion 1.5, SDXL, Gemini, Flux, and DALL.E by creating two datasets: one organized by race–gender categories and another using only nationality prompts. Using CLIP embeddings, we extract demographic representations through three methods: (i) a KNN classifier trained solely on model-generated demographic exemplars; (ii) a distance-based comparison of country-level embedding centroids to race–gender centroids; and (iii) a semantic layer from a multimodal LLM that infers demographic attributes from each portrait. An autoencoder provides latent-space visualizations for interpretable geometric inspection of cross-country patterns. Across models, we find systematic deviations from real census distributions, including consistent male overrepresentation (except Gemini, which skews female) and strong ethnic homogenization. Latin American countries are repeatedly mapped to Indigenous archetypes, while Canada and the United States are largely rendered with White prototypes. These distortions appear in both embedding geometry and LLM-based semantic judgments, indicating that bias affects low-level visual structure and high-level interpretation. By linking synthetic portraits to demographic baselines, this study offers a scalable method to audit representational fairness in AI models and stresses the need for culturally contextualized evaluation in responsible AI development.

Machine learning has transformed science and technology. In this article, we present a model-independent classifier that uses the k-Nearest Neighbors algorithm to classify phases of a model for which it has never been trained. This is done by studying three different spin-1 chains with some common phases: the XXZ chains with uniaxial single-ion-type anisotropy, the bond alternating XXZ chains, and the bilinear biquadratic chain. We show that the algorithm trained with two of these models can, with high probability, determine phases common to the third one. This is the first step towards a universal classifier, where an algorithm can recognize an arbitrary phase without knowing the Hamiltonian, since it knows only partial information about the quantum state.

Generative Artificial Intelligence has become pervasive in society, witnessing significant advancements in various domains. Particularly in the domain of Text-to-Image (TTI) models, Latent Diffusion Models (LDMs) showcase remarkable capabilities in generating visual content based on textual prompts. This paper addresses the potential of LDMs in representing local cultural concepts, historical figures, and endangered species. In this study, we use the cultural heritage of Rio Grande do Sul (RS), Brazil, as an illustrative case. Our objective is to contribute to the broader understanding of how generative models can help to capture and preserve the regional culture and historical identity. The article outlines the methodology, including subject selection, dataset creation, and fine-tuning process. The results showcase the picture generation alongside the challenges and feasibility of each concept. In conclusion, this work shows the power of these models to represent and preserve unique aspects of diverse regions and communities.