In Optica Quantum, Okinawa Institute of Science and Technology (OIST) researchers propose the first practical application of boson sampling for image identification, which is critical in a variety ...

When you listen to quantum enthusiasts, you may feel tempted to treat QML as an emerging silver bullet, which it isn’t.

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even ...

Amid the intensifying global competition in quantum computing, the launch of MLQES will undoubtedly accelerate the popularization and application of quantum computing technology.

Quantum computing appears on track to help companies in three main areas: optimization, simulation and machine learning. The appeal of quantum machine learning lies in its potential to tackle ...

The work, recently published in Nature Photonics ("Experimental quantum-enhanced kernel-based machine learning on a photonic processor"), shows promising new applications for optical quantum computers ...

This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms.

According to Franco Nori, "Machine learning can play a pivotal role in addressing large-scale quantum computation and optimization challenges.

Quantum Machines and Nvidia use machine learning to get closer to an error-corrected quantum computer Frederic Lardinois 10:00 AM PDT · November 2, 2024 ...

More information: Zhenghao Yin et al, Experimental quantum-enhanced kernel-based machine learning on a photonic processor, Nature Photonics (2025). DOI: 10.1038/s41566-025-01682-5 ...

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).