Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning

With quantum resources a precious commodity, their efficient use is highly desirable. Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Physical review letters Ročník 122; číslo 6; s. 060501
Hlavní autoři: Pepper, Alex, Tischler, Nora, Pryde, Geoff J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States American Physical Society 15.02.2019
Témata:
Artificial intelligence
Machine learning
Optimization
Quantum phenomena
Qubits (quantum computing)
ISSN:0031-9007, 1079-7114, 1079-7114
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:With quantum resources a precious commodity, their efficient use is highly desirable. Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a lower-dimensional space. Here, we experimentally realize a quantum autoencoder, which learns how to compress quantum data using a classical optimization routine. We demonstrate that when the inherent structure of the dataset allows lossless compression, our autoencoder reduces qutrits to qubits with low error levels. We also show that the device is able to perform with minimal prior information about the quantum data or physical system and is robust to perturbations during its optimization routine.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0031-9007
1079-7114
1079-7114
DOI:10.1103/PhysRevLett.122.060501