Quantum AI: Smarter & Greener Chips
- An international research team from the University of Vienna has demonstrated that even small quantum computers can improve the performance of machine learning algorithms.
- The team's work combined machine learning, which has already revolutionized various aspects of life and research, with quantum computing, a new computational paradigm.
- The researchers designed an experiment using a quantum photonic circuit built at the Politecnico di Milano in Italy.
Quantum Computing Enhances Machine Learning Performance
Updated June 09, 2025
An international research team from the University of Vienna has demonstrated that even small quantum computers can improve the performance of machine learning algorithms. Their experimental study, published in Nature Photonics, highlights potential applications for optical quantum computers in the field of quantum machine learning.
The team’s work combined machine learning, which has already revolutionized various aspects of life and research, with quantum computing, a new computational paradigm. This combination has led to the burgeoning field of quantum machine learning, which seeks to improve the speed, efficiency, and accuracy of algorithms by running them on quantum platforms. Achieving such improvements on current quantum computers remains a meaningful challenge.
The researchers designed an experiment using a quantum photonic circuit built at the Politecnico di Milano in Italy. This circuit ran a machine learning algorithm initially proposed by researchers at Quantinuum in the United Kingdom. The goal was to classify data points using a photonic quantum computer and isolate the contribution of quantum effects to understand the advantage over classical computers. The experiment revealed that even small quantum processors can outperform conventional algorithms.
“We found that for specific tasks our algorithm commits fewer errors than its classical Counterpart,” saeid Philip Walther from the University of Vienna,who led the project.
Zhenghao Yin, the first author of the publication, added that existing quantum computers can demonstrate good performance without necessarily exceeding state-of-the-art technology.
Another notable aspect of the research is that photonic platforms may consume less energy than standard computers. Co-author Iris Agresti emphasized that this could be crucial in the future, given the increasingly high energy demands of machine learning algorithms.
What’s next
The researchers’ findings could influence both quantum and standard computing. new algorithms inspired by quantum architectures could be designed to achieve better performance and reduce energy consumption, further advancing the field of quantum machine learning.