A study published in Nature Photonics demonstrates that small-scale photonic quantum computers can outperform classical systems in specific machine learning tasks. Researchers from the University of Vienna and collaborators used a quantum-enhanced algorithm on a photonic circuit to classify data more accurately than conventional methods. The goal was to classify data points using a photonic quantum computer and single out the contribution of quantum effects, to understand the advantage with respect to classical computers. The experiment showed that already small-sized quantum processors can peform better than conventional algorithms. “We found that for specific tasks our algorithm commits fewer errors than its classical Counterpart”, explains Philip Walther from the University of Vienna, lead of the project. “This implies that existing quantum computers can show good performances without necessarily going beyond the state-of-the-art Technology” adds Zhenghao Yin, first author of the publication in Nature Photonics. Another interesting aspect of the new research is that photonic platforms can consume less energy with respect to standard computers. “This could prove crucial in the future, given that machine learning algorithms are becoming infeasible, due to the too high energy demands”, emphasizes co-author Iris Agresti.