Explaining quantum correlations through evolution of causal models

TL;DR

This paper introduces a causal network framework to generalize Bell's theorem, using optimization and genetic algorithms to analyze quantum correlations and local causality assumptions.

Contribution

It presents a systematic method to relax local causality assumptions and quantitatively analyze quantum correlations through causal networks and optimization techniques.

Findings

Developed a novel genetic algorithm for causal network optimization.

Proved a new inequality generalizing Bell's theorem.

Demonstrated trade-offs between local causality assumptions and quantum data fit.

Abstract

We propose a framework for the systematic and quantitative generalization of Bell's theorem using causal networks. We first consider the multi-objective optimization problem of matching observed data while minimizing the causal effect of nonlocal variables and prove an inequality for the optimal region that both strengthens and generalizes Bell's theorem. To solve the optimization problem (rather than simply bound it), we develop a novel genetic algorithm treating as individuals causal networks. By applying our algorithm to a photonic Bell experiment, we demonstrate the trade-off between the quantitative relaxation of one or more local causality assumptions and the ability of data to match quantum correlations.