# Generalizable control for quantum parameter estimation through reinforcement learning

## TL;DR

This paper demonstrates that reinforcement learning can efficiently identify and generalize quantum control strategies to enhance parameter estimation precision, outperforming traditional methods.

## Contribution

It introduces reinforcement learning as a novel, efficient, and highly generalizable approach for quantum control in parameter estimation tasks.

## Key findings

- Reinforcement learning improves quantum parameter estimation precision.
- Neural networks trained on one parameter value generalize across a broad range.
- RL-based controls outperform conventional optimal control methods.

## Abstract

Measurement and estimation of parameters are essential for science and engineering, where one of the main quests is to find systematic schemes that can achieve high precision. While conventional schemes for quantum parameter estimation focus on the optimization of the probe states and measurements, it has been recently realized that control during the evolution can significantly improve the precision. The identification of optimal controls, however, is often computationally demanding, as typically the optimal controls depend on the value of the parameter which then needs to be re-calculated after the update of the estimation in each iteration. Here we show that reinforcement learning provides an efficient way to identify the controls that can be employed to improve the precision. We also demonstrate that reinforcement learning is highly generalizable, namely the neural network trained under one particular value of the parameter can work for different values within a broad range. These desired features make reinforcement learning an efficient alternative to conventional optimal quantum control methods.