Zapata (United States)

This article explores the theoretical foundations of International Relations (IR) and proposes a projection toward a new conceptual field: Interspatial Relations (ISR). In the face of increasing space exploration and the distant but possible contact with extraterrestrial life forms or civilizations, the concern arises to imagine a normative and political framework that allows for the regulation of interactions between actors—nations and/or individuals—beyond planet Earth. This framework also includes non-state actors for development and cooperation in interspatial dynamics, allowing for their integration into cosmic space as well as the recognition of their decentralized, functionally autonomous international action. This concept, proposed through comparative analysis and critical reflection, suggests that the founding principles of IR can serve as a basis for the development of ISR.

Manufacturing, consumer, transportation, and supply chain processes present significant challenges in monitoring, control, and design due to their inherently nonlinear nature and the difficulty of measuring critical variables in real time. The convergence of major innovations from the computer science field has the potential to revolutionize the engineering and control of complex industrial systems. Digital twinning and process simulation have been a staple of computers in process engineering for decades now. However, the advent of advanced sensor systems and big data integration, combined with generative AI and agentified AI (classic and quantum) systems, allows for much more granular and autonomous process control and real-time optimization of complex systems. Advanced process modeling, Agentic AI, and generative AI models have emerged as powerful tools to address the challenges of complex nonlinear systems. We propose here an integrated systems feedback and control architecture (SIC: Sense, Infer, Control) that leverages complementary process knowledge for enhanced real-time monitoring and decision-making, fully integrated into control system functions and the accompanying sensors. In this paper, we explore this integration of generative models in agentic AI ensembles into industrial processes through the lens of four recent industrial case studies: (1) the real-time optimization of motorsports strategy, (2) the development of indirect (soft) sensors for sustainable large-scale manufacturing operations, (3) the creation of sensor data-driven personalized health and cosmetic chemical formulations, and (4) the design of biomanufacturing systems using quantum and classic Agentic AI. These examples demonstrate how agentic and generative models, combined with full-scale process simulation and digital twinning, effectively augment process control, enabling advanced solutions for process optimization, quality improvement, and sustainable operations. The proposed SIC systems architecture serves to enhance process control automation by capturing complex nonlinear patterns and leveraging easily measurable variables. Generative models bridge gaps in process understanding, sensor technologies, control, and monitoring, offering actionable insights for efficient and informed decision-making across diverse industrial applications.

Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning. While possessing many desirable features arising from their quantum-inspired nature, tensor network generative models have previously been largely restricted to binary or categorical data, limiting their utility in real-world modeling problems. We overcome this by introducing a new family of tensor network generative models for continuous data, which are capable of learning from distributions containing continuous random variables. We develop our method in the setting of matrix product states, first deriving a universal expressivity theorem proving the ability of this model family to approximate any reasonably smooth probability density function with arbitrary precision. We then benchmark the performance of this model on several synthetic and real-world datasets, finding that the model learns and generalizes well on distributions of continuous and discrete variables. We develop methods for modeling different data domains, and introduce a trainable compression layer which is found to increase model performance given limited memory or computational resources. Overall, our methods give important theoretical and empirical evidence of the efficacy of quantum-inspired methods for the rapidly growing field of generative learning.

La Raicilla es una bebida artesanal análoga al mezcal, que se produce a partir de la destilación de agave. El objetivo de este trabajo es describir la estructura y organización de la cadena de valor de la raicilla producida en la Sierra Occidental de Jalisco, para mostrar los cambios en la producción y organización de los raicilleros en una línea de tiempo. La pregunta a responder es: ¿Cuáles son las características de la cadena de valor de la Raicilla? Se realizaron encuestas a los productores y se efectuaron entrevistas con actores clave. Se observó que la mayoría de los productores ingresan al mercado a través de una estructura vertical, y un número importante de ellos vende su producto en canales de ciclo corto. Se enfrentan a problemas como la escasez de materia prima, limitada formación técnica y falta de innovación en el procesamiento de la bebida. La cadena de valor está poco articulada, carece de cohesión interna y prevalece el individualismo. Es necesario innovar las estrategias comerciales y mejorar el desempeño de cada eslabón de la cadena, para fortalecer la presencia del destilado en el mercado del mezcal. Palabras clave: Raicilla, tradición y cultura, destilado artesanal, mezcales, cadena de valor, mercado.

Quantum metrology allows for measuring properties of a quantum system at the optimal Heisenberg limit. However, when the relevant quantum states are prepared using digital Hamiltonian simulation, the accrued algorithmic errors will cause deviations from this fundamental limit. In this work, we show how algorithmic errors due to Trotterized time evolution can be mitigated through the use of standard polynomial interpolation techniques. Our approach is to extrapolate to zero Trotter step size, akin to zero-noise extrapolation techniques for mitigating hardware errors. We perform a rigorous error analysis of the interpolation approach for estimating eigenvalues and time-evolved expectation values, and show that the Heisenberg limit is achieved up to polylogarithmic factors in the error. Our work suggests that accuracies approaching those of state-of-the-art simulation algorithms may be achieved using Trotter and classical resources alone for a number of relevant algorithmic tasks.

La agenda 2030 de la Asamblea General de Naciones Unidas (AGNU) propone el logro de condiciones de trabajo decente de todo trabajador en el mundo. El objetivo de esta investigacin es caracterizar el trabajo decente (TD) desde la perspectiva de los directivos de las micro y pequeas empresas en Cuernavaca, Morelos, Mxico. La hiptesis es que existen diferencias significativas en la percepcin del TD dependiendo del giro de la empresa y del nivel educativo del directivo. Se llev a cabo un estudio cuantitativo con una muestra de 408 directivos, a quienes se les aplic la escala de trabajo decente para empresarios (TDE). Los resultados indican que los directivos tienen una percepcin regular del TD sin diferencias significativas por giro o nivel educativo, lo que sugiere la homogeneidad en la percepcin de los sujetos y la necesidad de estrategias generales para mejorar la percepcin de los sujetos respecto de cada dimensin. El anlisis de las variables desde su conceptualizacin, si se considera a la parte directiva el enfoque se vuelve subjetivo, dado que la finalidad del empresario es percibir rentabilidad o ganancias, sin considerar racionalmente lo que como trabajo decente es determinado en los cuatro aspectos que la oit (2014) considera: derechos humanos, empleo productivo, proteccin social y dilogo social. En Cuernavaca las 3 actividades principales econmicas son comercio, fabricacin

Determining the properties of molecules and materials is one of the premier applications of quantum computing. A major question in the field is: how might we use imperfect near-term quantum computers to solve problems of practical value? We propose a quantum algorithm to estimate the properties of molecules using near-term quantum devices. The method is a recursive variational series estimation method, where we expand an operator of interest in terms of Chebyshev polynomials and evaluate each term in the expansion using a variational quantum algorithm. We test our method by computing the one-particle Green's function in the energy domain and the autocorrelation function in the time domain.

The first neonatal examination should be carried out from the delivery room to assess: 1. Initial neonatal well- being; 2. Vital signs at birth; 3. Somatometry; 4. Complete physical examination; 5. Verify anorectal permeability and 6. Detect congenital malformations, including Anorectal Malformations. The Clinical Practice Guideline and the experts in Anorectal Malformations recommend detection by introducing a rectal thermometer through the anus with which, at the same time, the rectal temperature is taken. However, there is no real well-structured technique for such a procedure. In Mexico, Latin America, and in countries with limited resources, a large part of the personnel who care for newborns, including doctors, nurses, and students, take rectal temperatures "in their own way" SIC, unaware of "a technique" and the "true objective" of this procedure, generating detection failure and risk of injuring the distal digestive tract by carrying out the procedure empirically and by the incorrect use of thermometers to take the rectal temperature. This issue has not been addressed for more than three decades nor has the way of 1. Checking anorectal patency changed; 2. Detect anorectal malformations; 3. Take the rectal temperature in neonates and infants. For this reason, our objective is to publicize our "New Technique to Verify Permeability and Anorectal Malformations and take the rectal temperature in neonates and infants", with which we carry out these three actions with a single non-empirical procedure of a scientifically medical nature.

De novo drug design with desired biological activitiesis crucialfor developing novel therapeutics for patients. The drug developmentprocess is time- and resource-consuming, and it has a low probabilityof success. Recent advances in machine learning and deep learningtechnology have reduced the time and cost of the discovery processand therefore, improved pharmaceutical research and development. Inthis paper, we explore the combination of two rapidly developing fieldswith lead candidate discovery in the drug development process. First,artificial intelligence has already been demonstrated to successfullyaccelerate conventional drug design approaches. Second, quantum computinghas demonstrated promising potential in different applications, suchas quantum chemistry, combinatorial optimizations, and machine learning.This article explores hybrid quantum-classical generative adversarialnetworks (GAN) for small molecule discovery. We substituted each elementof GAN with a variational quantum circuit (VQC) and demonstrated thequantum advantages in the small drug discovery. Utilizing a VQC inthe noise generator of a GAN to generate small molecules achievesbetter physicochemical properties and performance in the goal-directedbenchmark than the classical counterpart. Moreover, we demonstratethe potential of a VQC with only tens of learnable parameters in thegenerator of GAN to generate small molecules. We also demonstratethe quantum advantage of a VQC in the discriminator of GAN. In thishybrid model, the number of learnable parameters is significantlyless than the classical ones, and it can still generate valid molecules.The hybrid model with only tens of training parameters in the quantumdiscriminator outperforms the MLP-based one in terms of both generatedmolecule properties and the achieved KL divergence. However, the hybridquantum-classical GANs still face challenges in generating uniqueand valid molecules compared to their classical counterparts.

Recent advances in quantum computing devices have brought attention to hybrid quantum-classical algorithms like the Variational Quantum Eigensolver (VQE) as a potential route to practical quantum advantage in chemistry. However, it is not yet clear whether such algorithms, even in the absence of device error, could actually achieve quantum advantage for systems of practical interest. We have performed an exhaustive analysis to estimate the number of qubits and number of measurements required to compute the combustion energies of small organic molecules and related systems to within chemical accuracy of experimental values using VQE. We consider several key modern improvements to VQE, including low-rank factorizations of the Hamiltonian. Our results indicate that although these techniques are useful, they will not be sufficient to achieve practical quantum computational advantage for our molecular set, or for similar molecules. This suggests that novel approaches to operator estimation leveraging quantum coherence, such as Enhanced Likelihood Functions [arxiv:2006.09350, arxiv:2006.09349], may be required.