Photonic Quantum Chips by University of Vienna Make AI Smarter and More Energy-Efficient
Photonic Quantum Chips by University of Vienna Make AI Smarter and More Energy-Efficient
Date: June 8, 2025
Source: University of Vienna
A team of researchers has demonstrated that even small-scale quantum computers can improve machine learning performance using a novel photonic quantum circuit. The findings indicate that current quantum technology can already outperform classical systems in certain tasks. Importantly, the photonic approach used in this experiment could also significantly reduce energy usage, offering a more sustainable solution as machine learning’s power demands continue to rise.
One of today’s most exciting areas of research lies at the intersection of two transformative technologies: machine learning and quantum computing. This experimental study reveals that small-scale quantum processors can enhance machine learning algorithms, as demonstrated on a photonic quantum processor by an international team led by the University of Vienna. The research, published in Nature Photonics, opens promising new possibilities for optical quantum computing.
Recent breakthroughs have reshaped the development of next-generation technologies. While machine learning and AI have already revolutionized daily life and scientific work, quantum computing represents a completely new computational framework. The merging of these fields has given rise to Quantum Machine Learning—an area focused on finding improvements in speed, efficiency, or accuracy of algorithms running on quantum platforms.
Despite its potential, realizing these advantages with today’s quantum hardware remains a challenge. To address this, a team of researchers conducted an experimental study featuring a quantum photonic circuit constructed at the Politecnico di Milano in Italy. The machine learning algorithm tested was initially proposed by researchers from Quantinuum in the United Kingdom. The experiment focused on classifying data points using a photonic quantum computer and isolating the impact of quantum effects to determine the advantage over classical computing.
Results showed that even modestly sized quantum processors can outperform traditional algorithms.
“We found that for specific tasks our algorithm commits fewer errors than its classical counterpart,” said Philip Walther of 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,” added Zhenghao Yin, the study’s first author.
Another key outcome of the study is the lower energy consumption of photonic platforms compared to standard computers.
“This could prove crucial in the future, given that machine learning algorithms are becoming infeasible due to their extremely high energy demands,” emphasized co-author Iris Agresti.
This research advances both quantum computing and conventional computing by identifying tasks where quantum effects offer a real advantage. Moreover, the findings could inspire new algorithms modeled after quantum architectures, potentially achieving better performance while reducing energy use.
About the University of Vienna
Founded in 1365, the University of Vienna is one of the oldest and most prestigious universities in Europe. It is a leader in quantum research, particularly in photonic and experimental quantum physics.
About Politecnico di Milano
Politecnico di Milano is Italy’s largest technical university, known for excellence in engineering, architecture, and industrial design. Its quantum optics and photonics groups contribute to cutting-edge developments in quantum technologies.
About Quantinuum
Quantinuum is a global quantum computing company formed by the combination of Honeywell Quantum Solutions and Cambridge Quantum. Headquartered in the UK and US, it develops quantum hardware and software with applications in cybersecurity, chemistry, and AI.
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