PYSQT’s seminars: Vicente Soloviev
Seminar given by Vicente P. Soloviev, from the Universidad Politécnica de Madrid: “Quantum approximate optimization algorithm for Bayesian network structure learning”.
29th March, 16:00, online.
Abstract:
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.
