Accurate phonon blockade detector composed of a quadratically coupled optomechanical system

TL;DR

This paper proposes a machine learning-based method to detect phonon blockade in quadratically coupled optomechanical systems, overcoming measurement challenges and demonstrating high accuracy and robustness.

Contribution

It introduces a novel detection scheme using neural networks to identify phonon blockade in nonlinear optomechanical systems.

Findings

High detection accuracy for phonon blockade

Robustness against system parameter disturbances

Effective for strong photon blockade detection

Abstract

The observation of phonon blockade in a nanomechanical oscillator is clear evidence of its quantum nature. However, it is still a severe challenge to measure the strong phonon blockade in an optomechanical system with effective nonlinear coupling. In this paper, we propose a theoretical proposal for detecting the phonon blockade effect in a quadratically coupled optomechanical system by exploiting supervised machine learning. The detected optical signals are injected into the neural network as the input, while the output is the mechanical equal-time second-order correlation. Our results show our scheme performs superior performance on detecting phonon blockade. Specifically, it is efficient for nonlinear coupling systems; it performs a high precision for strong photon blockade; it is robust against the small disturbance of system parameters. Our work opens a promising way to build a phonon blockade detector.