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...

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Bibliographic Details
Published in:Physical review letters Vol. 122; no. 6; p. 060501
Main Authors: Pepper, Alex, Tischler, Nora, Pryde, Geoff J.
Format: Journal Article
Language:English
Published: United States American Physical Society 15.02.2019
Subjects:
Artificial intelligence
Machine learning
Optimization
Quantum phenomena
Qubits (quantum computing)
ISSN:0031-9007, 1079-7114, 1079-7114
Online Access:Get full text
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Summary: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.
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ISSN:0031-9007
1079-7114
1079-7114
DOI:10.1103/PhysRevLett.122.060501