ArticleNature communications2023
Realizing a deep reinforcement learning agent for real-time quantum feedback.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Article
- Information-Theoretic Framework for Quantum State Purification and Error Correction via Symmetric Subspace Projection.Entropy (Basel, Switzerland) · 2026Article
- Artificial intelligence for quantum computing.Nature communications · 2025Review
- Machine learning for estimation and control of quantum systems.National science review · 2025Review
- Near real-time online reinforcement learning with synchronous or asynchronous updates.Scientific reports · 2025Article
- Efficient learning for linear properties of bounded-gate quantum circuits.Nature communications · 2025Article
- Reinforcement learning in cold atom experiments.Nature communications · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Realizing the full potential of quantum technologies requires precise real-time control on time scales much shorter than the coherence time. Model-free reinforcement learning promises to discover efficient feedback strategies from scratch without relying on a description of the quantum system. However, developing and training a reinforcement learning agent able to operate in real-time using feedback has been an open challenge. Here, we have implemented such an agent for a single qubit as a sub-microsecond-latency neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit and train the agent based solely on measurements. Our work is a first step towards adoption of reinforcement learning for the control of quantum devices and more generally any physical device requiring low-latency feedback.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.