Samenvatting
Summary form only given. The efficient characterization and validation of the underlying model of a quantum physical system is a central challenge in the development of quantum devices and for our understanding of foundational quantum physics. However, the impossibility to efficiently predict the behaviour of complex quantum models on classical machines makes this challenge to be intractable to classical approaches. Quantum Hamiltonian Learning (QHL) [1, 2] combines the capabilities of quantum information processing and classical machine learning to allow the efficient characterisation of the model of quantum systems. In QHL the behaviour of a quantum Hamiltonian model is efficiently predicted by a quantum simulator, and the predictions are contrasted with the data obtained from the quantum system to infer the system Hamiltonian via Bayesian methods.
Originele taal-2 | Engels |
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Titel | European Quantum Electronics Conference, EQEC 2017 |
Plaats van productie | Piscataway |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Aantal pagina's | 1 |
ISBN van elektronische versie | 978-1-5090-6736-7 |
ISBN van geprinte versie | 978-1-5090-6737-4 |
DOI's | |
Status | Gepubliceerd - 1 jan. 2017 |
Evenement | 2017 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference, CLEO/Europe-EQEC 2017 - Messe Munich, Munich, Duitsland Duur: 25 jun. 2017 → 29 jun. 2017 http://2007.cleoeurope.org/ |
Congres
Congres | 2017 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference, CLEO/Europe-EQEC 2017 |
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Verkorte titel | CLEO/Europe-EQEC 2017 |
Land/Regio | Duitsland |
Stad | Munich |
Periode | 25/06/17 → 29/06/17 |
Ander | CLEO 2007, Baltimore, Maryland, USA |
Internet adres |