A small-scale quantum device developed by researchers at the University of Vienna has outperformed advanced classical machine learning algorithms—including some used in today’s leading AI systems—using just two photons and a glass chip.
The experiment suggests that useful quantum advantage could arrive far sooner than previously thought, not in massive future machines but in today’s modest photonic setups.
The team’s six-mode processor doesn’t rely on raw speed to beat traditional systems. Instead, it harnesses a uniquely quantum property: the way identical particles interfere. This interference naturally computes mathematical structures known as permanents, which are computationally expensive for classical systems.
By embedding these quantum calculations into a pattern-recognition task, the researchers consistently achieved higher classification accuracy across multiple datasets.
Crucially, the device operates with extreme energy efficiency, offering a promising route to sustainable AI. Co-author Iris Agresti highlighted the growing energy costs of modern machine learning and pointed to photonic quantum systems as a potential solution.
These early results could pave the way for new applications in areas where training data is limited and classical methods fall short—redefining the future of AI and quantum computing alike.
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