Dynamical model selection near the quantum-classical boundary

TL;DR

This paper introduces a general method for selecting between quantum and classical dynamical models using experimental time-trace data, enhancing the ability to distinguish quantum effects in various systems.

Contribution

It presents a new model selection approach that improves quantum-classical discrimination, applicable in real-time and post-processing, with specific conditions for optimal hypothesis testing.

Findings

Maximized quantum hypothesis testing capabilities.

Set temperature and measurement efficiency thresholds.

Demonstrated method with levitated optomechanical systems.

Abstract

We discuss a general method of model selection from experimentally recorded time-trace data. This method can be used to distinguish between quantum and classical dynamical models. It can be used in post-selection as well as for real-time analysis, and offers an alternative to statistical tests based on state-reconstruction methods. We examine the conditions that optimize quantum hypothesis testing, maximizing one's ability to discriminate between classical and quantum models. We set upper limits on the temperature and lower limits on the measurement efficiencies required to explore these differences, using a novel experiment in levitated optomechanical systems as an example.