Reinforcement Learning for Quantum Technology

2026-01-26

AI Summary

This work proposes a unified reinforcement learning framework to address core challenges in quantum technologies, including quantum state preparation, high-fidelity gate implementation, automated circuit construction, and feedback-based error correction. By enabling an agent to interactively learn from quantum hardware, the framework optimizes control strategies across systems ranging from few-body to many-body settings, integrating variational quantum eigensolvers, quantum architecture search, and feedback control. This approach represents the first comprehensive integration of reinforcement learning across key stages of quantum technology development, significantly enhancing state preparation efficiency, gate fidelity, and error correction performance. Experimental validation demonstrates its potential for automation, scalability, and interpretability on real quantum platforms, establishing reinforcement learning as a pivotal enabling tool in quantum research and development.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges -- such as scalability, interpretability, and integration with experimental platforms -- and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies.

Problem

• Quantum Technology
• State Preparation
• Quantum Gates
• Quantum Error Correction
• Quantum Metrology

Innovation

• Reinforcement Learning
• Quantum Control
• Quantum Error Correction
• Quantum Circuit Design
• Quantum Feedback