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

doi:10.1109/CLEOE-EQEC.2017.8087392

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

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.

Original language	English
Title of host publication	European Quantum Electronics Conference, EQEC 2017
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	1
ISBN (Electronic)	978-1-5090-6736-7
ISBN (Print)	978-1-5090-6737-4
DOIs	https://doi.org/10.1109/CLEOE-EQEC.2017.8087392
Publication status	Published - 1 Jan 2017
Event	2017 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference, CLEO/Europe-EQEC 2017 - Messe Munich, Munich, Germany Duration: 25 Jun 2017 → 29 Jun 2017 http://2007.cleoeurope.org/

Conference

Conference	2017 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference, CLEO/Europe-EQEC 2017
Abbreviated title	CLEO/Europe-EQEC 2017
Country/Territory	Germany
City	Munich
Period	25/06/17 → 29/06/17
Internet address	http://2007.cleoeurope.org/

Bibliographical note

Only abstract.

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10.1109/CLEOE-EQEC.2017.8087392

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Paesani, S., Wang, J., Santagati, R., Knauer, S., Gentile, A. A., Wiebe, N., Petruzzella, M., Laing, A., Rarity, J. G., O'Brien, J. L., & Thompson, M. G. (2017). Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond. In European Quantum Electronics Conference, EQEC 2017 Article F81 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CLEOE-EQEC.2017.8087392

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

abstract = "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.",

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

note = "Only abstract.; 2017 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference, CLEO/Europe-EQEC 2017, CLEO/Europe-EQEC 2017 ; Conference date: 25-06-2017 Through 29-06-2017",

year = "2017",

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doi = "10.1109/CLEOE-EQEC.2017.8087392",

language = "English",

isbn = "978-1-5090-6737-4 ",

booktitle = "European Quantum Electronics Conference, EQEC 2017",

publisher = "Institute of Electrical and Electronics Engineers",

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Paesani, S, Wang, J, Santagati, R, Knauer, S, Gentile, AA, Wiebe, N, Petruzzella, M, Laing, A, Rarity, JG, O'Brien, JL & Thompson, MG 2017, Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond. in European Quantum Electronics Conference, EQEC 2017., F81, Institute of Electrical and Electronics Engineers, Piscataway, 2017 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference, CLEO/Europe-EQEC 2017, Munich, Germany, 25/06/17. https://doi.org/10.1109/CLEOE-EQEC.2017.8087392

Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond. / Paesani, Stefano; Wang, Jianwei; Santagati, Raffaele et al.
European Quantum Electronics Conference, EQEC 2017. Piscataway: Institute of Electrical and Electronics Engineers, 2017. F81.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Paesani, Stefano

AU - Wang, Jianwei

AU - Santagati, Raffaele

AU - Knauer, Sebastian

AU - Gentile, Andreas A.

AU - Wiebe, Nathan

AU - Petruzzella, Maurangelo

AU - Laing, Anthony

AU - Rarity, John G.

AU - O'Brien, Jeremy L.

AU - Thompson, M. G.

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N2 - 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.

AB - 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.

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BT - European Quantum Electronics Conference, EQEC 2017

PB - Institute of Electrical and Electronics Engineers

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Y2 - 25 June 2017 through 29 June 2017

ER -

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

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