Observing Schr\"odinger's Cat with Artificial Intelligence: Emergent Classicality from Information Bottleneck

TL;DR

This paper demonstrates how classical reality emerges from quantum data using a generative language model constrained by an information bottleneck, revealing the quantum-classical boundary and potential for AI in quantum physics.

Contribution

It introduces a method to train generative models on quantum measurement data to study the emergence of classicality from quantum systems.

Findings

Classical reality emerges in language models due to the information bottleneck.

The quantum-classical boundary depends on system size and the agent’s information processing power.

Potential for using NISQ device data to train models for quantum operator representation.

Abstract

We train a generative language model on the randomized local measurement data collected from Schr\"odinger's cat quantum state. We demonstrate that the classical reality emerges in the language model due to the information bottleneck: although our training data contains the full quantum information about Schr\"odinger's cat, a weak language model can only learn to capture the classical reality of the cat from the data. We identify the quantum-classical boundary in terms of both the size of the quantum system and the information processing power of the classical intelligent agent, which indicates that a stronger agent can realize more quantum nature in the environmental noise surrounding the quantum system. Our approach opens up a new avenue for using the big data generated on noisy intermediate-scale quantum (NISQ) devices to train generative models for representation learning of quantum operators, which might be a step toward our ultimate goal of creating an artificial intelligence quantum physicist.