Some of the most important open questions in physics, such as why the Universe contains more matter than antimatter and whether certain fundamental symmetries of nature are broken, motivate searches for new particles and forces beyond the Standard Model of particle physis. Many of these symmetry violating effects are expected to be extremely small, meaning that discovering them depends on...
The temperature of the cometary background is a parameter associated with the physics of cometary microwave background radiation, which, analogously to the cosmic microwave background radiation, is based on Max Planck's theory of black body radiation and its postulate. of energy quantization, concepts that originated quantum physics.
The physics of cometary microwave background radiation is...
This work presents an automatic framework for extracting the electrical properties of biological tissues by solving a multi-relaxation Cole–Cole model using a Bayesian-initialized, hierarchical hybrid multi-level optimization strategy. We analyze complex capacitance in the Nyquist plane, where the real and imaginary components reveal circular features; in our laboratory we have repeatedly...
Cold hybrid atom-ion systems offer a unique platform to explore charge-neutral interactions with high precision and to investigate the behavior of a charged impurity embedded in a quantum-degenerate atomic gas. Achieving the fully quantum s-wave regime in such systems remains a significant challenge, requiring a deep understanding of—and control over—various loss mechanisms.
Recent studies...
One-dimensional spin-½ models present rich dynamics as well as non-trivial phase diagrams, allowing us to illustrate fundamental properties of many-body quantum systems, such as quantum phase transitions, both in equilibrium and dynamical. The behavior of such transitions becomes particularly interesting when a quantum quench protocol is applied to systems with broken-symmetry phases, such as...
Quantum phase estimation (QPE) is a key quantum algorithm for performing chemical and solid-state computations in fault-tolerant quantum computers. Recently, several authors have proposed alternatives to QPE that have advantages in early fault-tolerant devices, including shorter circuits and better suitability for error mitigation techniques. However, practical implementations of the algorithm...
Este trabajo evalúa, el potencial de las redes neuronales cuánticas informadas por la física (QPINN) para resolver ecuaciones diferenciales parciales (EDPs), con énfasis en la arquitectura híbrida QCPINN (Farea et al., 2025). La metodología consiste en una revisión crítica con comparación por arquitectura (en circuitos de variable discreta), esquema de \textit{embedding} y topología; en la...
El presente trabajo tiene como objetivo principal explorar la intersección entre los principios fundamentales de la mecánica cuántica, específicamente la no-localidad, y el campo emergente de la ciberseguridad. La motivación central surge de la creciente amenaza que la computación cuántica, a través de algoritmos como el de Shor, presenta para los protocolos de encriptación clásicos (como...
We study a geometric--algebraic representation of finite-dimensional pure quantum states based on the Majorana stellar map, which encodes a spin-$j$ state as the set of roots of a complex polynomial on the Riemann sphere. For each quantum state we construct the associated Majorana polynomial, whose roots fully determine the state up to normalization and global phase.
Within this...
We present a graph neural network (GNN) framework for predicting molecular
energies from molecular orbital graphs. Our approach leverages information from
one- and two-electron integrals encoded as graph features, together with pooling
strategies that map orbital-level predictions to molecular energies. The proposed
approach was tested on 6 diatomic molecules, a total of 132 geometries,...
La retrodispersión coherente de luz (Coherent Backscattering, CBS) es un fenómeno óptico que surge en medios desordenados debido a la interferencia constructiva de trayectorias múltiples recíprocas, análogo óptico de la localización débil cuántica ampliamente estudiada en el transporte de electrones en medios desordenados. Este efecto produce un aumento de intensidad en la dirección de...
La ponencia aborda el papel transformador de la inteligencia artificial (IA) en la gestión sustentable de los servicios ecosistémicos del suelo, específicamente en la interfaz entre las cuencas hidrográficas y las áreas urbanas. La relación entre estos dos entornos es crucial para mantener la calidad del agua, la biodiversidad y la resiliencia ante el cambio climático. La IA emerge como una...
The interaction between light and disordered media can be described as a stochastic process where multiple scattering and interference determine the macroscopic propagation of electromagnetic waves. In this work, we develop a numerical framework based on Monte Carlo methods to model light transport focusing on the Rayleigh–Debye regime, considering spherical scatterers and their associated...
Recent advances in Transmission Electron Microscopy (TEM), including aberration correction, cryogenic stabilization, and ultrafast electron pulses, have pushed spatial and temporal resolution to unprecedented levels. Yet extending this precision to the detection of single spins of the investigated samples and their magnetic dynamics remains a major challenge. Here, we present a theoretical and...
This study presents the development, training, and validation of a 1D Convolutional Neural Network (CNN) designed to estimate redshift directly from spectral data. To support this, a synthetic dataset of 75000 spectra was generated and partitioned into training (70%), validation (15%), and testing (15%) subsets. These spectra simulate black-body radiation with temperatures ranging from 3000 K...
Physics-informed neural networks (PINNs) are a versatile methodology that integrates deep learning techniques with the solution of dynamics governed by differential equations, with applications across multiple areas of physics. In this work, we design and optimize a PINN-based control scheme that, instead of directly solving the dynamics of the system, uses the integration of the evolution...
Non linear excitations in biochemistry have the potential to pave the way for understanding some of the most important and relevant quantum biological phenomena such as energy localization, transfer and high efficiency energy storage, problems approached from the perspective of quantum thermodynamics from different angles. One of the greatest mysteries where quantum biology and quantum...
Reliable isotope identification is essential in radiation monitoring and nuclear engineering, especially when measurements are affected by attenuation, noise, and low resolution detectors. This project explores a quantum machine learning approach for classifying isotopes using attenuated radiation data acquired with a custom built Geiger based detection system. We implement a variational...
We present an implementation of neural network based quantum state tomography following the framework of Koutný et al. (2022). The approach learns an inverse mapping from POVM measurement outcomes to the Cholesky parameters of a density matrix, ensuring positivity and physical consistency. Our work reproduces the original results for dimensionsd=2,5,7 using large scale synthetic datasets...
El crecimiento exponencial de modelos fundacionales ha logrado un rendimiento notable en múltiples dominios, sin embargo, este progreso conlleva un costo computacional y energético cada vez más prohibitivo. Las leyes de escalamiento demuestran que el rendimiento de los modelos mejora como función potencial del tamaño del modelo, tamaño del conjunto de datos y cómputo de entrenamiento,...
SIMULACIÓN CUÁNTICA DE LA DINÁMICA ENTRÓPICA EN EL MODELO DE ASOCIACIÓN-DISOCIACIÓN DE H2 : IMPLEMENTACIÓN EN QISKIT
Departamento de Física, Universidad Nacional de Colombia, Medellín, Colombia.
Alejandra Vidal Girón
avidalg@unal.edu.co
Alejandro Sánchez Marín
alsanchezm@unal.edu.co
Trabajo supervisado por:
Prof. Alcides Montoya Cañola
Universidad Nacional de Colombia, sede...
Existe un interés creciente en la computación neuromórfica que ha impulsado el estudio de circuitos fotónicos capaces de emular el funcionamiento de las redes neuronales tradicionales. El cambio de plataformas electrónicas a ópticas aprovecha la naturaleza ondulatoria de la luz para realizar operaciones matemáticas en paralelo, a altas velocidades y con bajo consumo energético. Uno de estos...
This work examines entanglement dynamics in random quantum circuits to better understand information propagation, thermalization, and complexity growth in many-body quantum systems. Using the minimal-cut formalism within the KPZ universality class, we develop an efficient graph-based algorithm that yields a classical upper bound on bipartite entanglement growth. Our results confirm KPZ scaling...
We investigate the BB84 Quantum Key Distribution (QKD) protocol enhanced with a Vacuum+Weak decoy-state configuration through both simulation and a low-cost optical experiment. An interactive simulation built with Qiskit and Streamlit modeled key exchange under ideal, noisy, and adversarial conditions, while a separate decoy-state simulation quantified photon-number yields to reveal...
