Quantum Photonic Chip Outperforms Classical AI in Accuracy and Energy Efficiency

Photonic quantum chips are making AI smarter and greener

Recent scientific breakthroughs have reshaped the development of future technologies. On the one hand, machine learning and artificial intelligence have already revolutionized our lives from everyday tasks to scientific research. On the other hand, quantum computing has emerged as a new paradigm of computation. From the combination of these promising two fields, a new research line has opened up: Quantum Machine Learning. This field aims at finding potential enhancements in the speed, efficiency or accuracy of algorithms when they run on quantum platforms. It is however still an open challenge, to achieve such an advantage on current technology quantum computers. This is where an international team of researchers took the next step and designed a novel experiment carried out by scientists from the University of Vienna. The set-up 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). The goal was to classify data points using a photonic quantum computer and single out the contribution of quantum effects, to understand the advantage with respect to classical computers. The experiment showed that already small-sized quantum processors can peform 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. "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. Another interesting aspect of the new research is that photonic platforms can consume less energy with respect to standard computers. "This could prove crucial in the future, given that machine learning algorithms are becoming infeasible, due to the too high energy demands," emphasizes co-author Iris Agresti. The result of the researchers has an 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.

Quantum machine learning: Small-scale photonic quantum processor can already outperform classical counterparts

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already small-scale quantum computers can boost the performance of machine learning algorithms. This was demonstrated on a photonic quantum processor by an international team of researchers at the University of Vienna. The work, published in Nature Photonics, shows promising new applications for optical quantum computers. Recent scientific breakthroughs have reshaped the development of future technologies. On the one hand, machine learning and artificial intelligence have already revolutionized our lives from everyday tasks to scientific research. On the other hand, quantum computing has emerged as a new paradigm of computation. From the combination of these promising two fields, a new research line has opened up: Quantum Machine Learning. This field aims at finding potential enhancements in the speed, efficiency or accuracy of algorithms when they run on quantum platforms. It is, however, still an open challenge to achieve such an advantage on current technology quantum computers. This is where an international team of researchers took the next step and designed a novel experiment carried out by scientists from the University of Vienna. The set-up 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). The goal was to classify data points using a photonic quantum computer and single out the contribution of quantum effects, to understand the advantage with respect to classical computers. The experiment showed that already 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. "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. Another interesting aspect of the new research is that photonic platforms can consume less energy with respect to standard computers. "This could prove crucial in the future, given that machine learning algorithms are becoming infeasible, due to the too high energy demands," emphasizes co-author Iris Agresti. The result of the researchers has an 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.

Tiny quantum processor outshines classical AI in accuracy, energy use

The finding offers a glimpse into a faster, greener era within the relatively new research field of Quantum Machine Learning, a space gaining momentum across both academia and industry. The new study combines quantum computing and machine learning, two of the most disruptive technologies of our time. Recent advances in both fields are reshaping the technological frontier. While AI is already embedded in everything from personal assistants to scientific research, quantum computing promises a fundamentally new way of processing information. Their intersection has given rise to a rapidly growing field: quantum machine learning. This emerging discipline explores whether quantum systems can improve the speed, accuracy, or efficiency of machine learning algorithms. However, proving such an advantage on today's limited quantum hardware remains a major challenge -- one that researchers are just beginning to tackle. Conducted by an international team led by the University of Vienna, the experiment used a photonic quantum processor to classify data points, an essential task in modern AI systems. The researchers found that the quantum system outperformed its classical counterpart, making fewer errors -- a rare, real-world glimpse of quantum advantage with current hardware.