A team of international researchers led by the University of Vienna has achieved a significant breakthrough in quantum computing and artificial intelligence, demonstrating that even small-scale quantum computers can provide practical benefits for machine learning applications.
The study, published in Nature Photonics on June 8, 2025, utilized a novel photonic quantum circuit to implement a quantum-enhanced kernel-based machine learning algorithm. The researchers showed that their quantum approach outperforms state-of-the-art classical methods such as Gaussian and neural tangent kernels in binary classification tasks.
"We found that for specific tasks our algorithm commits fewer errors than its classical counterpart," explains Professor Philip Walther from the University of Vienna, who led 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.
The experimental setup featured a quantum photonic circuit built at the Politecnico di Milano (Italy), running a machine learning algorithm first proposed by researchers at Quantinuum (United Kingdom). The system uses quantum interference and single-photon coherence to achieve superior accuracy in data classification tasks.
Beyond improved accuracy, this photonic approach offers significant energy efficiency advantages. As machine learning applications become increasingly complex and energy-intensive, quantum photonic processors could provide a sustainable alternative. "This could prove crucial in the future, given that machine learning algorithms are becoming infeasible due to too high energy demands," emphasizes co-author Iris Agresti.
The research has implications beyond quantum computing, as it identifies specific tasks that benefit from quantum effects and could inspire new classical algorithms with better performance and reduced energy consumption. This represents a significant step toward practical quantum advantage in AI applications, bridging the gap between theoretical quantum computing and real-world implementation.