Skip to content

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond. / Paesani, Stefano; Wang, Jianwei; Santagati, Raffaele; Knauer, Sebastian; Gentile, Antonio Andreas; Wiebe, Nathan; Petruzzella, Maurangelo; Laing, Anthony; Rarity, John; O'Brien, Jeremy; Thompson, Mark.

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. Institute of Electrical and Electronics Engineers (IEEE), 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

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. Institute of Electrical and Electronics Engineers (IEEE), 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

APA

Paesani, S., Wang, J., Santagati, R., Knauer, S., Gentile, A. A., Wiebe, N., ... 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 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CLEOE-EQEC.2017.8087392

Vancouver

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. Institute of Electrical and Electronics Engineers (IEEE). 2017 https://doi.org/10.1109/CLEOE-EQEC.2017.8087392

Author

Paesani, Stefano ; Wang, Jianwei ; Santagati, Raffaele ; Knauer, Sebastian ; Gentile, Antonio Andreas ; Wiebe, Nathan ; Petruzzella, Maurangelo ; Laing, Anthony ; Rarity, John ; O'Brien, Jeremy ; Thompson, Mark. / 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. Institute of Electrical and Electronics Engineers (IEEE), 2017.

Bibtex

@inproceedings{adbaaece17c44bd08cdc243ee32fb692,
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",
year = "2017",
month = "10",
day = "30",
doi = "10.1109/CLEOE-EQEC.2017.8087392",
language = "English",
isbn = "9781509067374",
booktitle = "2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
address = "United States",

}

RIS - suitable for import to EndNote

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

SN - 9781509067374

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

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -