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Experimental quantum Hamiltonian learning
With the help of a quantum simulator and Bayesian inference it is possible to determine the unknown Hamiltonian of a quantum system. An experiment demonstrates this using a photonic quantum simulator and a solid-state system. The efficient characterization of quantum systems 1 , 2 , 3 , the verifica...
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Published in: | Nature physics 2017-06, Vol.13 (6), p.551-555 |
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Main Authors: | , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | With the help of a quantum simulator and Bayesian inference it is possible to determine the unknown Hamiltonian of a quantum system. An experiment demonstrates this using a photonic quantum simulator and a solid-state system.
The efficient characterization of quantum systems
1
,
2
,
3
, the verification of the operations of quantum devices
4
,
5
,
6
and the validation of underpinning physical models
7
,
8
,
9
, are central challenges for quantum technologies
10
,
11
,
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and fundamental physics
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,
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. The computational cost of such studies could be improved by machine learning enhanced by quantum simulators
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,
16
. Here we interface two different quantum systems through a classical channel—a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen–vacancy centre—and use the former to learn the Hamiltonian of the latter via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately 10
−5
. Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model. We implement an interactive version of the protocol and experimentally show its ability to characterize the operation of the quantum photonic device. |
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ISSN: | 1745-2473 1745-2481 |
DOI: | 10.1038/nphys4074 |