
Open access
Date
2019-11-11Type
- Journal Article
ETH Bibliography
yes
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Abstract
The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information processing as well as for reliable quantum memories. Inferring the experimentally realized Hamiltonian through a scalable number of measurements constitutes the challenging task of Hamiltonian learning. In particular, assessing the quality of the implementation of topological codes is essential for quantum error correction. Here, we introduce a neural-net-based approach to this challenge. We capitalize on a family of exactly solvable models to train our algorithm and generalize to a broad class of experimentally relevant sources of errors. We discuss how our algorithm scales with system size and analyze its resilience toward various noise sources. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000377386Publication status
publishedExternal links
Journal / series
Physical Review ResearchVolume
Pages / Article No.
Publisher
American Physical SocietyOrganisational unit
08714 - Gruppe Huber
Funding
182240 - Metamaterials, Waves and Topology (SNF)
771503 - Topological Mechanical Metamaterials (EC)
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ETH Bibliography
yes
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