Optimal control of linear Gaussian quantum systems via quantum learning control

TL;DR

This paper introduces a quantum-learning-control method using gradient descent for efficiently controlling linear Gaussian quantum systems, enabling rapid cooling and entanglement generation surpassing traditional methods.

Contribution

It presents a flexible, gradient-based quantum control approach tailored for LGQ systems, capable of achieving faster cooling and entanglement than existing techniques.

Findings

Achieves ground-state cooling faster than sideband cooling.

Generates optomechanical entanglement exceeding steady-state levels.

Demonstrates effectiveness in high thermal phonon regimes.

Abstract

Efficiently controlling linear Gaussian quantum (LGQ) systems is a significant task in both the study of fundamental quantum theory and the development of modern quantum technology. Here, we propose a general quantum-learning-control method for optimally controlling LGQ systems based on the gradient-descent algorithm. Our approach flexibly designs the loss function for diverse tasks by utilizing first- and second-order moments that completely describe the quantum state of LGQ systems. We demonstrate both deep optomechanical cooling and large optomechanical entanglement using this approach. Our approach enables the fast and deep ground-state cooling of a mechanical resonator within a short time, surpassing the limitations of sideband cooling in the continuous-wave driven strong-coupling regime. Furthermore, optomechanical entanglement could be generated remarkably fast and surpass several times the corresponding steady-state entanglement, even when the thermal phonon occupation reaches one hundred. This work will not only broaden the application of quantum learning control, but also open an avenue for optimal control of LGQ systems.