Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond

Stefano Paesani, Jianwei Wang, Raffaele Santagati, Sebastian Knauer, Andreas A. Gentile, Nathan Wiebe, Maurangelo Petruzzella, Anthony Laing, John G. Rarity, Jeremy L. O'Brien, M. G. Thompson

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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-2Engels
TitelEuropean Quantum Electronics Conference, EQEC 2017
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's1
ISBN van elektronische versie978-1-5090-6736-7
ISBN van geprinte versie978-1-5090-6737-4
DOI's
StatusGepubliceerd - 1 jan 2017
EvenementEuropean Quantum Electronics Conference, EQEC 2017 - Munich, Duitsland
Duur: 25 jun 201729 jun 2017

Congres

CongresEuropean Quantum Electronics Conference, EQEC 2017
LandDuitsland
StadMunich
Periode25/06/1729/06/17

Bibliografische nota

Only abstract.

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