Quantum estimation, control and learning: opportunities and challenges
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Abstract (arXiv)
The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jan 2022.
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Citing papers per year: 2023: 2; 2024: 1.
3 papers.
- 2023Tomography of Quantum States from Structured Measurements via quantum-aware transformercited by 8
- 2023Auxiliary Task-based Deep Reinforcement Learning for Quantum Controlcited by 2
- 2024Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Controlcited by 1
- 2024Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control21 Aug 2024
- 2023Tomography of Quantum States from Structured Measurements via quantum-aware transformer9 May 2023
- 2023Auxiliary Task-based Deep Reinforcement Learning for Quantum Control28 Feb 2023