The increased availability of specialized quantum hardware has made possible the
exploration of the field by the general scientific community. Enterprises such as IBM, Google …

Probabilistic graphical models such as Bayesian networks are widely used to model
stochastic systems to perform various types of analysis such as probabilistic prediction, risk …

As the risk associated with wildfires in the western United States increases every year, it
becomes fundamental to research alternatives to both improve the resilience and reliability …

We present a method to model a discretized time evolution of probabilistic networks on gate-
based quantum computers. We consider networks of nodes, where each node can be in one …

Studying the propagation of failure probabilities in interconnected systems like that of
electrical distribution networks is traditionally performed by means of Monte Carlo …

Studying the propagation of failure probabilities in interconnected systems such as electrical
distribution networks is traditionally performed by means of Monte Carlo simulations. In this …

Bayesian networks are a type of probabilistic graphical model widely used to characterize
various real-world problem scenarios due to their ability to model probabilistic dependence …

In recent years, there has been a significant upsurge in the interest surrounding Quantum
machine learning, with researchers actively developing methods to leverage the power of …

Bayesian Networks (BN) are probabilistic graphical models that are widely used for
uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian …

The advent of quantum computing, capitalizing on quantum–mechanical phenomena like
superposition and entanglement, promises a paradigm shift in computational capabilities. As …