Hamiltonian learning and certification using quantum resources

In recent years quantum simulation has made great strides, culminating in experiments that existing supercomputers cannot easily simulate. Although this raises the possibility that special purpose analog quantum simulators may be able to perform computational tasks that existing computers cannot, it...

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Bibliographic Details
Published in:Physical review letters 2014-05, Vol.112 (19), p.190501-190501, Article 190501
Main Authors: Wiebe, Nathan, Granade, Christopher, Ferrie, Christopher, Cory, D G
Format: Article
Language:English
Subjects:
Analog computers
Certification
Computer simulation
Learning
Mathematical models
Simulators
Supercomputers
Tasks
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Summary:In recent years quantum simulation has made great strides, culminating in experiments that existing supercomputers cannot easily simulate. Although this raises the possibility that special purpose analog quantum simulators may be able to perform computational tasks that existing computers cannot, it also introduces a major challenge: certifying that the quantum simulator is in fact simulating the correct quantum dynamics. We provide an algorithm that, under relatively weak assumptions, can be used to efficiently infer the Hamiltonian of a large but untrusted quantum simulator using a trusted quantum simulator. We illustrate the power of this approach by showing numerically that it can inexpensively learn the Hamiltonians for large frustrated Ising models, demonstrating that quantum resources can make certifying analog quantum simulators tractable.
ISSN:0031-9007
1079-7114
DOI:10.1103/physrevlett.112.190501