Decision-making under uncertainty: a quantum value operator approach

TL;DR

This paper introduces a quantum expected value framework for decision-making under uncertainty, modeling subjective beliefs with quantum density operators and optimizing decision trees via genetic programming.

Contribution

It presents a novel quantum value operator approach and a method to automatically generate quantum decision trees for decision-making.

Findings

Quantum density operators effectively model subjective beliefs.

Genetic programming optimizes quantum decision trees.

The approach offers a new perspective on decision-making under uncertainty.

Abstract

We propose a quantum expected value theory for decision-making under uncertainty. Quantum density operator as value operator is proposed to simulate people's subjective beliefs. Value operator guides people to choose corresponding actions based on their subjective beliefs through objective world. The value operator can be constructed from quantum gates and logic operations as a quantum decision tree. The genetic programming is used to optimize and auto-generate quantum decision trees.