A groundbreaking study by an international research team led by the University of Vienna has demonstrated that even small-scale quantum computers can significantly boost machine learning performance using a novel photonic quantum circuit.
The researchers have shown that today's quantum technology isn't just experimental—it can already outperform classical systems in specific tasks. The experiment used a photonic quantum computer to classify data points and demonstrated that small-sized quantum processors can perform 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.
The experimental setup 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). "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.
One particularly promising aspect of this research is that photonic platforms can consume significantly less energy compared to standard computers. "This could prove crucial in the future, given that machine learning algorithms are becoming infeasible due to their too high energy demands," emphasizes co-author Iris Agresti. Since only light, and no current, is flowing through the circuit, photonic chips have lower cooling requirements. Combining this with higher performance and compute density leads to substantial energy savings. Some photonic AI accelerators promise to consume up to 30 times less energy than a Graphics Processing Unit (GPU).
The result has 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. This breakthrough demonstrates that small-scale photonic quantum computers can outperform classical systems in specific machine learning tasks, with researchers using a quantum-enhanced algorithm on a photonic circuit to classify data more accurately than conventional methods.
As AI systems continue to grow in complexity and power demands, this research opens a path toward more sustainable and powerful AI technologies that leverage quantum advantages today, not just in some theoretical future. The integration of quantum photonics and machine learning represents one of the most promising frontiers in computing technology, with immediate practical applications already emerging.