Quantum Lyapunov control with machine learning

TL;DR

This paper introduces a machine learning-based method for quantum Lyapunov control that adapts control parameters to initial states using neural networks, improving efficiency without extra computational costs.

Contribution

It presents a novel initial-state-adaptive quantum control strategy employing supervised learning neural networks for optimized control parameter selection.

Findings

Neural networks effectively select control schemes for different initial states.

The approach reduces computational resources needed for quantum control.

Demonstrated improved control performance with machine learning integration.

Abstract

Quantum state engineering is a central task in Lyapunov-based quantum control. Given different initial states, better performance may be achieved if the control parameters, such as the Lyapunov function, are individually optimized for each initial state, however, at the expense of computing resources. To tackle this issue, we propose an initial-state-adaptive Lyapunov control strategy with machine learning, specifically, artificial neural networks trained through supervised learning. Two designs are presented and illustrated where the feedforward neural network and the general regression neural network are used to select control schemes and design Lyapunov functions, respectively. Since the sample generation and the training of neural networks are carried out in advance, the initial-state-adaptive Lyapunov control can be implemented without much increase of computational resources.