1. Quantum approximate optimization algorithm for Bayesian network structure learning
- Author
-
Vicente P. Soloviev, Concha Bielza, and Pedro Larrañaga
- Subjects
FOS: Computer and information sciences ,Informática ,Quantum Physics ,Computer Science - Machine Learning ,Matemáticas ,FOS: Physical sciences ,Statistical and Nonlinear Physics ,Machine Learning (cs.LG) ,Theoretical Computer Science ,Electronic, Optical and Magnetic Materials ,Modeling and Simulation ,Signal Processing ,Electrical and Electronic Engineering ,Quantum Physics (quant-ph) - Abstract
Bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently, quantum technologies offer a wide range of advantages that can be exploited to solve optimization tasks that cannot be addressed in an efficient way when utilizing classic computing approaches. 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, by employing $3n(n-1)/2$ qubits, where $n$ is the number of nodes in the Bayesian network to be learned. 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, and the results justified the use of variational quantum algorithms for solving the Bayesian network structure learning problem.
- Published
- 2022