Dynamical model selection near the quantum-classical boundary

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.

Optimal control for feedback cooling in cavityless levitated optomechanics

“…The dynamics of the continuously monitored trapped particle is described by the following master equation [56]:…”

Trends in Quantum Nanophotonics

“…The experiments are, not surprisingly, very challenging. They will require the optimization of electromechanical and optomechanical design, signal processing and control, and the application of a formal hypothesis testing framework [ 166 ] is appropriate in this domain. However, the potential payoff in an enhanced understanding of fundamental physics is great, and there is the likelihood that enhanced sensors will also be developed alongside this work.…”

Measurement and feedback for cooling heavy levitated particles in low-frequency traps

“…Most related experiments have so far assessed success based on a temperature associated with the measured motional power spectrum. Alternatively, there are recent proposals for distinguishing quantum motion via dynamical model selection using solely position measurements [26]. They look to identify quantum statistics from a series of position measurements after introducing a small perturbation to the trapping potential.…”