5 citations · 14 across the 14 of their papers we have counts for
14 papers
Classical and Quantum Machine Learning for Population-Level Prediction of Heat-Related Physiological Events
Saul Gonzalez-Bermejo, Tommaso Albrigi, Borja Vazquez-Morado +4
Predicting heat-related physiological events at the population level is challenging due to the complex interactions among climatic, demographic, and socioeconomic factors, as well…
Hydration Monitoring Using Urinary Biomarkers: A Hybrid Classical Quantum Predictive Modeling Framework
Saul Gonzalez-Bermejo, Tommaso Albrigi, Borja Vazquez-Morado +7
Hydration status is a key physiological indicator associated with cellular homeostasis, renal function, and overall health. Recent advances in smart sensing environments enable pas…
Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
Kai Hu, Parfait Atchade-Adelomou, Carlo Adornetto +5
Understanding human behavior in urban environments is a crucial field within city sciences. However, collecting accurate behavioral data, particularly in newly developed areas, pos…
QMProt: A Comprehensive Dataset of Quantum Properties for Proteins
Laia Coronas Sala, Parfait Atchade-Adelemou
We introduce Quantum Mechanics for Proteins (QMProt), a dataset developed to support quantum computing applications in protein research. QMProt contains precise quantum-mechanical…
Propagational Proxy Voting
Yasushi Sakai, Parfait Atchade-Adelomou, Ryan Jiang +3
This paper proposes a voting process in which voters allocate fractional votes to their expected utility in different domains: over proposals, other participants, and sets containi…
Quantum improvement in Spatial Discretization
Saul Gonzalez, Parfait Atchade-Adelomou
Quantum algorithms have begun to surpass classical ones in several computation fields, yet practical application remains challenging due to hardware and software limitations. Here,…
Efficient Quantum Modular Arithmetics for the ISQ Era
Parfait Atchade-Adelomou, Saul Gonzalez
As we venture into the Intermediate-Scale Quantum (ISQ) era, the proficiency of modular arithmetic operations becomes pivotal for advancing quantum cryptographic algorithms. This s…
Quantum Machine Learning hyperparameter search
S. Consul-Pacareu, R. Montaño, Kevin Rodriguez-Fernandez +4
This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a…
Fourier series weight in quantum machine learning
Parfait Atchade-Adelomou, Kent Larson
In this work, we aim to confirm the impact of the Fourier series on the quantum machine learning model. We will propose models, tests, and demonstrations to achieve this objective.…
Quantum Algorithms for solving Hard Constrained Optimisation Problems
Parfait Atchade-Adelomou
The thesis deals with Quantum Algorithms for solving Hard Constrained Optimization Problems. It shows how quantum computers can solve difficult everyday problems such as finding th…
GPS: A new TSP formulation for its generalizations type QUBO
Saúl González-Bermejo, Guillermo Alonso-Linaje, Parfait Atchade-Adelomou
We propose a new Quadratic Unconstrained Binary Optimization (QUBO) formulation of the Travelling Salesman Problem (TSP), with which we overcame the best formulation of the Vehicle…
EVA: a quantum Exponential Value Approximation algorithm
Guillermo Alonso-Linaje, Parfait Atchade-Adelomou
VQE is currently one of the most widely used algorithms for optimizing problems using quantum computers. A necessary step in this algorithm is calculating the expectation value giv…
qRobot: A Quantum computing approach in mobile robot order picking and batching problem solver optimization
Parfait Atchade-Adelomou, Guillermo Alonso-Linaje, Jordi Albo-Canals +1
This article aims to bring quantum computing to robotics. A quantum algorithm is developed to minimize the distance travelled in warehouses and distribution centres where order pic…
Quantum Enhanced Filter: QFilter
Parfait Atchade-Adelomou, Guillermo Alonso-Linaje
Convolutional Neural Networks (CNN) are used mainly to treat problems with many images characteristic of Deep Learning. In this work, we propose a hybrid image classification model…