Approximation of quantum control correction scheme using deep neural networks

DOI10.1007/s11128-019-2240-7zbMath1504.81091arXiv1803.05193OpenAlexW3098098561MaRDI QIDQ2100739

L. Banchi, Przemysław Sadowski, M. Ostaszewski, Jarosław Adam Miszczak

Publication date: 25 November 2022

Published in: Quantum Information Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.05193

zbMATH Keywords

recurrent neural networkquantum dynamicsquantum controldeep learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Quantum control (81Q93) Computational stability and error-correcting codes for quantum computation and communication processing (81P73)

Related Items (1)

Geometrical versus time-series representation of data in quantum control learning

Uses Software

• LSTM
• QuTiP
• TensorFlow
• GitHub

Cites Work

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• How to generate random matrices from the classical compact groups
• Quantum control and information processing
• Generating three-qubit quantum circuits with neural networks
• Generating and using truly random quantum states in \texttt{Mathematica}
• Quantum control with spectral constraints
• Lively quantum walks on cycles
• Statistical Machine Translation
• Quantum machine learning: a classical perspective
• Robust quantum gates for open systems via optimal control: Markovian versus non-Markovian dynamics
• Introduction to Quantum Control and Dynamics

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