# Learning Quantum Systems

@inproceedings{Gebhart2022LearningQS, title={Learning Quantum Systems}, author={Valentin Gebhart and Raffaele Santagati and Antonio A. Gentile and Erik M. Gauger and David A. Craig and Natalia Ares and Leonardo Banchi and Florian Marquardt and Luca Pezz{\`e} and Cristian Bonato}, year={2022} }

Quantum technologies hold the promise to revolutionise our society with ground-breaking applications in secure communication, high-performance computing and ultra-precise sensing. One of the main features in scaling up quantum technologies is that the complexity of quantum systems scales exponentially with their size. This poses severe challenges in the efﬁcient calibration, benchmarking and validation of quantum states and their dynamical control. While the complete simulation of large-scale…

## 3 Citations

### Artificial Intelligence and Machine Learning for Quantum Technologies

- Computer SciencePhysical Review A
- 2023

This perspective article showcases in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artiﬁcial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation.

### Online adaptive estimation of decoherence timescales for a single qubit

- Physics
- 2022

The rate of decoherence is a critical parameter in the performance of quantum bits, memories and sensors. Fast estimation of these timescales is necessary for eﬃcient characterisation of large arrays…

### Real-time frequency estimation of a qubit without single-shot-readout

- Physics
- 2022

Inbar Zohar1,∗ Yoav Romach, Muhammad Junaid Arshad, Nir Halay, Niv Drucker, Rainer Stöhr, Andrej Denisenko, Yonatan Cohen, Cristian Bonato, and Amit Finkler Department of Chemical and Biological…

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