# A Reinforcement Learning approach for Quantum State Engineering

## TL;DR

This paper demonstrates how classical reinforcement learning can be applied to quantum state engineering, enabling automated preparation of complex quantum states with potential scalability for larger systems.

## Contribution

It introduces a systematic RL-based framework for quantum state engineering, including algorithms for continuous state spaces and scalability considerations.

## Key findings

- Successfully prepares arbitrary two-qubit entangled states
- Generalizes human-designed solutions using RL
- Discusses scalability for large entangled states

## Abstract

Machine learning (ML) has become an attractive tool in information processing, however few ML algorithms have been successfully applied in the quantum domain. We show here how classical reinforcement learning (RL) could be used as a tool for quantum state engineering (QSE). We employ a measurement based control for QSE where the action sequences are determined by the choice of the measurement basis and the reward through the fidelity of obtaining the target state. Our analysis clearly displays a learning feature in QSE, for example in preparing arbitrary two-qubit entangled states. It delivers successful action sequences, that generalise previously found human solutions from exact quantum dynamics. We provide a systematic algorithmic approach for using RL algorithms for quantum protocols that deal with non-trivial continuous state (parameter) space, and discuss on scaling of these approaches for preparation of arbitrarily large entangled (cluster) states.