Recent scientific breakthroughs have reshaped the development of future technologies. On the one hand, machine learning and artificial intelligence have already revolutionized our lives from everyday tasks to scientific research. On the other hand, quantum computing has emerged as a new paradigm of computation.

From the combination of these promising two fields, a new research line has opened up: Quantum Machine Learning. Among its challenges, this field aims at finding potential enhancements in the speed, efficiency or accuracy of algorithms when they run on quantum platforms. It is however still an open challenge, to achieve such an advantage on current technology quantum computers,

This is where an international team took the next step and designed a new experiment carried out by scientists from the University of Vienna. The set-up features a quantum photonic circuit built at the Politecnico di Milano (Italy), which runs a machine learning algorithm first proposed by researchers working at Quantinuum (United Kingdom). 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. 

This result has an impact both on quantum computation, since it identifies tasks that benefit from quantum effects, as well as on standard computing. Indeed, new algorithms, inspired by quantum architectures could be designed, reaching better performances and reducing energy consumption.

• Z. Yin, I. Agresti, G. de Felice, D. Brown, A. Toumi, C. Pentangelo, S. Piacentini, A. Crespi, F. Ceccarelli, R. Osellame, B. Coecke, P. Walther, 
  Experimental quantum-enhanced kernel-based machine learning on a photonic processor
  Nature Photonics (2025).

Contact

Prof. Philip Walther
philip.walther@univie.ac.at

Dr. Iris Agresti
iris.agresti@univie.ac.at