In this paper, we present AcausalNets.jl - a library supporting inference in a quantum generalization of Bayesian networks and their application to quantum games. The proposed solution is based on modern approach to numerical computing provided by Julia language. The library provides a high-level functions for Bayesian inference that can be applied to both classical and quantum Bayesian networks.
BT - Computational Science – ICCS 2019 CY - Cham LA - eng N2 -In this paper, we present AcausalNets.jl - a library supporting inference in a quantum generalization of Bayesian networks and their application to quantum games. The proposed solution is based on modern approach to numerical computing provided by Julia language. The library provides a high-level functions for Bayesian inference that can be applied to both classical and quantum Bayesian networks.
PB - Springer International Publishing PP - Cham PY - 2019 SN - 978-3-030-22750-0 T2 - Computational Science – ICCS 2019 TI - Support for High-Level Quantum Bayesian Inference ER -