An Algorithm to Calculate a Quantum Probability Space

TL;DR

This paper introduces a Quantum Probability Space (QPS) and an algorithm to compute it, enabling better data modeling across multiple contexts in fields like IR and ML, where uncertainty and context variability are significant.

Contribution

The paper proposes a novel Quantum Probability Space and provides an algorithm for its calculation from multi-context data, improving probabilistic modeling in uncertain environments.

Findings

The QPS effectively models data from multiple contexts.

The algorithm enables practical computation of QPS.

Web application implementation demonstrates usability.

Abstract

In this paper we address the problem of using one probability space for estimating parameters and predicting future data when the observed data come from multiple contexts and thus from distinct spaces. We explain that a set-based probabilistic space might be suboptimal in the case of multiple contexts. To overcome suboptimality and reconcile multiple contexts in one space, the paper introduces the Quantum Probability Space (QPS). We also present an algorithm to calculate the QPS for data observed from multiple contexts and provide a web application that implements the algorithm. The QPS has application in Information Retrieval (IR), Machine Learning (ML) and in any domain where items should optimally be ranked and classified by some properties but under conditions of uncertainty due to context.