Vicente P. Soloviev (UPM)
Bayesian network structure learning is an NP-hard problem that has been faced by a
number of traditional approaches in recent decades. In this work, a specific type of
variational quantum algorithm, the quantum approximate optimization algorithm, was
used to solve the Bayesian network structure learning problem. Our results showed that
the quantum approximate optimization algorithm approach offers competitive results
with state-of-the-art methods and quantitative resilience to quantum noise. The
approach was applied to a cancer benchmark problem.