Building a human-like observer using deep learning in an extended Wigner's friend experiment

TL;DR

This paper introduces a novel framework using deep learning to simulate human-like observers in an extended Wigner's friend experiment, aiming to explore quantum superposition at macroscopic levels.

Contribution

It presents a new experimental setup integrating neural networks into quantum measurement scenarios, with innovative metrics for evaluating artificial observers.

Findings

Demonstrated the framework through simulations.

Developed three new analytical metrics.

Provided criteria for artificial intelligence as a bona fide observer.

Abstract

There has been a longstanding demand for artificial intelligence with human-level cognitive sophistication to address loopholes in Bell-type experiments. In this study, we propose a novel experimental framework that integrates advanced deep learning techniques, employing neural network-based artificial intelligence in an extended Wigner's friend experiment. We demonstrate the framework through simulations and introduce three new analytical metrics-morphing polygons, averaged Shannon entropy, and probability density maps-to evaluate the results. These results can be used to determine whether our artificial intelligence qualifies as a bona fide observer and whether superposition applies to macroscopic systems, including observers.