Reinforcement learning to learn quantum states for Heisenberg scaling accuracy

TL;DR

This paper introduces a reinforcement learning-based meta-learning approach to efficiently learn quantum states, achieving near-Heisenberg limit accuracy and demonstrating generalization from small to larger quantum systems.

Contribution

It presents a novel RL-driven meta-learning model with an action repetition strategy that enhances data efficiency and generalizes across different quantum system sizes.

Findings

RL significantly improves sample efficiency in learning quantum states.

Achieves infidelity scaling close to the Heisenberg limit.

Trained on 3-qubit states, the RL agent generalizes to 5-qubit states.

Abstract

Learning quantum states is a crucial task for realizing quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. We propose a meta-learning model that utilizes reinforcement learning (RL) to optimize the process of learning quantum states. To improve the data efficiency of the RL, we introduce an action repetition strategy inspired by curriculum learning. The RL agent significantly improves the sample efficiency of learning random quantum states, and achieves infidelity scaling close to the Heisenberg limit. We also show that the RL agent trained using 3-qubit states can generalize to learning up to 5-qubit states. These results highlight the utility of RL-driven meta-learning to enhance the efficiency and generalizability of learning quantum states. Our approach can be applied to improve quantum control, quantum optimization, and quantum machine learning.