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:arXiv.org 2019-02
Main Authors: Pepper, Alex, Tischler, Nora, Pryde, Geoff J
Format: Article
Language:English
Subjects:
Artificial intelligence
Data compression
Machine learning
Optimization
Quantum phenomena
Qubits (quantum computing)
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 data set 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.
ISSN:2331-8422
DOI:10.48550/arxiv.1810.01637