Training a Quantum Neural Network Requires Only A Small Amount of Data


A new research paper titled “Generalization in quantum machine learning from few training data” was published by researchers at Technical University of Munich, Munich Center for Quantum Science and Technology (MCQST), Caltech, and Los Alamos National Lab.

“Many people believe that quantum machine learning will require a lot of data. We have rigorously shown that for many relevant problems, this is not the case,” said Lukasz Cincio, a quantum theorist at Los Alamos National Laboratory and co-author of the paper. “This provides new hope for quantum machine learning. We’re closing the gap between what we have today and what’s needed for quantum advantage, when quantum computers outperform classical computers,” according to this Los Alamos National Lab news summary.

Find the technical paper here. Published August 22, 2022.

Caro, M.C., Huang, HY., Cerezo, M. et al. Generalization in quantum machine learning from few training data. Nat Commun 13, 4919 (2022).

Related Reading
Quantum Machine Learning: Security Threats & Lines Of Defense
Penn State researchers report on using quantum machine learning (QML) in the HW security domain.
Experimental Photonic Quantum Memristor
Researchers demonstrate a new device, called the quantum memristor, combining two worlds.
Quantum Computers And CMOS Semiconductors: A Review And Future Predictions
Well-understood materials will likely play a key role in the quantum era.

Leave a Reply

(Note: This name will be displayed publicly)