Support for High-Level Quantum Bayesian Inference

被引：0

作者：

Przewiezlikowski, Marcin ^{[1
]}

Grabowski, Michal ^{[1
]}

Kurzyk, Dariusz ^{[2
]}

Rycerz, Katarzyna ^{[1
]}

机构：

[1] AGH Univ Sci & Technol, Inst Comp Sci, Al Mickiewicza 30, PL-30059 Krakow, Poland

[2] Polish Acad Sci, Inst Theoret & Appl Informat, Baltycka 5, PL-44100 Gliwice, Poland

来源：

COMPUTATIONAL SCIENCE - ICCS 2019, PT V | 2019年 / 11540卷

关键词：

Quantum Bayesian networks; Quantum games; Julia language;

D O I：

10.1007/978-3-030-22750-0_76

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：764 / 770

页数：7

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