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2022
Presentation
Title

Machine learning and simulation on NISQ devices

Title Supplement
Presentation held at QBN Meeting on Quantum Computing Software & Algorithms - Looking Beyond User Access & Simulators, 25.11.2022, Espoo
Abstract
Our team is investigating many fields, which hold a potential for a practical quantum advantage. In this presentation, we show-case some of our recent achievements within the realm of quantum simulation and quantum machine learning. Specifically, we presented some of our results within Munich Quantum Valley project, such as a recent publication in Noise impact investigation for VQE algorithm, results in Quantum reinforcement learning and current state-of-the-art in Quantum kernel methods. A recent publication in Quantum Convolutional NN was presented as well.
Author(s)
Sakhnenko, Alona
Fraunhofer-Institut für Kognitive Systeme IKS  
Lorenz, Jeanette Miriam  orcid-logo
Fraunhofer-Institut für Kognitive Systeme IKS  
Conference
Meeting on Quantum Computing Software & Algorithms - Looking Beyond User Access & Simulators 2022  
File(s)
Download (2.02 MB)
Rights
Use according to copyright law
DOI
10.24406/publica-665
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • quantum machine learning

  • quantum simulation

  • noisy-intermediate-scale quantum

  • NISQ

  • Variational Quantum Eigensolver

  • VQE

  • quantum reinforcement learning

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