Researchers at the University of Tokyo have identified a precise sweet spot where quantum reservoir computing, a machine learning approach that treats quantum systems as computational engines, reaches ...

Reservoir computing is a promising machine learning-based approach for the analysis of data that changes over time, such as ...

By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...

Artificial intelligence (AI) has become integral to our daily lives, from virtual assistants like Siri to personalized recommendations on Netflix. As AI technology advances, quantum machine learning ...

Scientists are exploring the potential of quantum machine learning. But whether there are useful applications for the fusion of artificial intelligence and quantum computing is unclear. Call it the ...

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 problems ...

The quantum tangent kernel method is a mathematical approach used to understand how fast and how well quantum neural networks can learn. A quantum neural network is a machine learning model that runs ...

Explore the future of the Quantum 2.0 market, set to expand from $3 billion in 2026 to over $50 billion by 2036, driven by quantum computing, sensing, and communications. This seismic technological ...

D-Wave Quantum (NYSE:QBTS) agreed to acquire Quantum Circuits, Inc., aiming to combine annealing and gate-model quantum ...

Orbital-free approach enables precise, stable, and physically meaningful calculation of molecular energies and electron ...