Quantum Bayesian decision-making*

TL;DR

This paper introduces a quantum decision-making process based on Bayesian networks, demonstrating computational advantages and a prototype implementation in Qiskit, advancing quantum AI methods for reasoning under uncertainty.

Contribution

It proposes a fully quantum Bayesian decision-making framework with improved algorithms and a proof-of-concept implementation, enhancing quantum AI capabilities.

Findings

Quantum approach offers computational advantages over classical methods.

Prototype implementation in Qiskit demonstrates feasibility.

Improved algorithms for quantum Bayesian inference.

Abstract

As a compact representation of joint probability distributions over a dependence graph of random variables, and a tool for modelling and reasoning in the presence of uncertainty, Bayesian networks are of great importance for artificial intelligence to combine domain knowledge, capture causal relationships, or learn from incomplete datasets. Known as a NP-hard problem in a classical setting, Bayesian inference pops up as a class of algorithms worth to explore in a quantum framework. This paper explores such a research direction and improves on previous proposals by a judicious use of the utility function in an entangled configuration. It proposes a completely quantum mechanical decision-making process with a proven computational advantage. A prototype implementation in Qiskit (a Python-based program development kit for the IBM Q machine) is discussed as a proof-of-concept.