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; Antonio Andreas Gentile; Nathan Wiebe; Maurangelo Petruzzella; Anthony Laing; John Rarity; Jeremy O'Brien; Mark 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, Antonio Andreas Gentile, Nathan Wiebe, Maurangelo Petruzzella, Anthony Laing, John Rarity, Jeremy O'Brien, Mark Thompson

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

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	2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)
Subtitle of host publication	Proceedings of a meeting held 25-29 June 2017, Munich, Germany
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1
Number of pages	1
Volume	October 2017
ISBN (Electronic)	9781509067367
ISBN (Print)	9781509067374
DOIs	https://doi.org/10.1109/CLEOE-EQEC.2017.8087392
Publication status	Published - 30 Oct 2017
Event	Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC 2017 - Munich, Germany Duration: 25 Jun 2017 → 29 Jun 2017 http://www.cleoeurope.org/

Conference

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

Research Groups and Themes

Bristol Quantum Information Institute
QETLabs
Photonics and Quantum

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10.1109/CLEOE-EQEC.2017.8087392

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Persistent link

Two level systems for scalable quantum processors
Rarity, J. G. (Principal Investigator)
1/04/15 → 31/03/20
Project: Research
Quantum Optics for Integrated Photonic Technologies
Rarity, J. G. (Principal Investigator)
16/06/14 → 15/06/19
Project: Research
Fabricating a photonic quantum computer.
O'Brien, J. L. (Principal Investigator)
1/04/13 → 31/03/18
Project: Research

Cite this

Paesani, S., Wang, J., Santagati, R., Knauer, S., Gentile, A. A., Wiebe, N., Petruzzella, M., Laing, A., Rarity, J., O'Brien, J., & Thompson, M. (2017). Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond. In 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC): Proceedings of a meeting held 25-29 June 2017, Munich, Germany (Vol. October 2017, pp. 1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CLEOE-EQEC.2017.8087392

Paesani, Stefano ; Wang, Jianwei ; Santagati, Raffaele et al. / Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond. 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC): Proceedings of a meeting held 25-29 June 2017, Munich, Germany. Vol. October 2017 Institute of Electrical and Electronics Engineers (IEEE), 2017. pp. 1

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

abstract = "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, {Antonio Andreas} and Nathan Wiebe and Maurangelo Petruzzella and Anthony Laing and John Rarity and Jeremy O'Brien and Mark Thompson",

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

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Paesani, S, Wang, J, Santagati, R, Knauer, S, Gentile, AA, Wiebe, N, Petruzzella, M, Laing, A , Rarity, J, O'Brien, J & Thompson, M 2017, Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond. in 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC): Proceedings of a meeting held 25-29 June 2017, Munich, Germany. vol. October 2017, Institute of Electrical and Electronics Engineers (IEEE), pp. 1, Conference on Lasers and Electro-Optics Europe & 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.
2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC): Proceedings of a meeting held 25-29 June 2017, Munich, Germany. Vol. October 2017 Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 1.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond

AU - Paesani, Stefano

AU - Wang, Jianwei

AU - Santagati, Raffaele

AU - Knauer, Sebastian

AU - Gentile, Antonio Andreas

AU - Wiebe, Nathan

AU - Petruzzella, Maurangelo

AU - Laing, Anthony

AU - Rarity, John

AU - O'Brien, Jeremy

AU - Thompson, Mark

PY - 2017/10/30

Y1 - 2017/10/30

N2 - 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 - 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.

U2 - 10.1109/CLEOE-EQEC.2017.8087392

DO - 10.1109/CLEOE-EQEC.2017.8087392

M3 - Conference Contribution (Conference Proceeding)

SN - 9781509067374

VL - October 2017

SP - 1

BT - 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC 2017

Y2 - 25 June 2017 through 29 June 2017

ER -

Paesani S, Wang J, Santagati R, Knauer S, Gentile AA, Wiebe N et al. Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond. In 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC): Proceedings of a meeting held 25-29 June 2017, Munich, Germany. Vol. October 2017. Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 1 doi: 10.1109/CLEOE-EQEC.2017.8087392

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

Abstract

Conference

Research Groups and Themes

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Projects

Two level systems for scalable quantum processors

Quantum Optics for Integrated Photonic Technologies

Fabricating a photonic quantum computer.

Cite this