(AdvancedScienceNews) The field of quantum machine learning has gained much attention because of its advantages for machine learning calculations by means of quantum parallelism and entanglement. Among other topics, quantum autoencoders—counterparts to classical autoencoders, which are neural networks that can learn efficient codings of large datasets—are being explored due to their potential in efficiently compressing quantum information.
A group of researchers from the University of the Basque Country UPV/EHU and Shanghai University have analyzed the possible implementation of approximate quantum adders capable of producing superpositions to a certain fidelity.
The team, which included Professor Enrique Solano and Professor Xi Chen, produced a quantum autoencoder in the Rigetti cloud quantum computer via quantum adders based on the initial work by Lamata. This pioneering experiment may prove useful in efficiently compressing quantum information and could be important in emerging photonic setups.
“We have implemented a quantum autoencoder via quantum adders in the Rigetti cloud quantum computer,”says Dr. Lamata. “This kind of approach may be useful in the future for efficient compression and communication of quantum information, possibly in the framework of quantum satellites and quantum drones”.