# Quantum estimation, control and learning: opportunities and challenges

arXiv: https://arxiv.org/abs/2201.05835 · HTML: https://mlsanity.com/paper/2201.05835/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [Tomography of Quantum States from Structured Measurements via quantum-aware transformer](https://mlsanity.com/paper/2305.05433/) (2023)
- [Auxiliary Task-based Deep Reinforcement Learning for Quantum Control](https://mlsanity.com/paper/2302.14312/) (2023)
- [Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control](https://mlsanity.com/paper/2408.11328/) (2024)

## 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.