One of the hottest research topics today is machine learning and quantum computing, two combinations of the latest technological breakthroughs. Experimental studies have shown that already small quantum computers can improve the performance of machine learning algorithms. This was demonstrated on a photonic quantum processor by an international team of researchers at the University of Vienna. The work, recently published in Nature Photonics, shows promise in new applications for optical quantum computers.
Recent scientific breakthroughs have reconstructed future technology developments. On the one hand, machine learning and artificial intelligence have already revolutionized our lives, from everyday work to scientific research. Quantum computing, on the other hand, is emerging as a new paradigm of computation.
From the combination of these two promising areas, a new line of research has been opened: quantum machine learning. This field is intended to find potential enhancements to the speed, efficiency, or accuracy of the algorithm when run on the Quantum platform. However, achieving these advantages with current technology quantum computers remains an open challenge.
This is where an international team of researchers took the next step and designed a new experiment carried out by scientists at the University of Vienna. The setup features a quantum photonic circuit built in Polipoliti Nico Dilano (Italy) and runs the machine learning algorithm first proposed by researchers working in Quantinuum (UK). The goal was to use Photonic quantum computers to classify data points and classify quantum effects contributions into a single one to understand the benefits of classical computers. This experiment showed that already small quantum processors are superior to traditional algorithms. “For certain tasks, we found that the algorithms have fewer errors than the classic counterparts,” explains Philip Walther, the University of Vienna, the project lead. “This means that existing quantum computers can perform well without necessarily surpassing cutting-edge technologies,” adds Zhenghao Yin, the first author of a Nature Photonics publication.
Another interesting aspect of the new research is that photonic platforms can consume less energy with regard to standard computers. “This could prove to be important in the future given that machine learning algorithms are becoming unfeasible due to the high energy demand,” stressed co-author Iris Agresti.
Researcher's findings have an impact on both quantum computation, as they identify both tasks that benefit from quantum effects and standard computing. In fact, we designed new algorithms inspired by quantum architectures to achieve better performance and reduce energy consumption.
“Experimental quantum reinforced kernel-based machine learning in photonic processors,” Z. Yin, I. Agresti, G. DeFelice, D. Brown, A. Toumi, C. Pentantelo, S. Piacentini, A. Crespi, F. Ceccarelli, R. Sellame, B. Coecke, P. Whalther. Nature Photonics (2025).
Data points classification can be performed via photonic quantum computers, increasing the accuracy of traditional methods. C: Iris Agresti
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