Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning. (arXiv:1810.01637v2 [quant-ph] UPDATED)

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.

Article web page: