QNNs achieved 95 percent accuracy, while QSVMs reached 94 percent, both surpassing classical machine learning methods, which ...

This quantum output was mixed with the original image data and analyzed using a simple linear classifier. This hybrid strategy maintained information while outperforming all similarly sized machine ...

New tools that simplify quantum complexity are reshaping physics research, drawing fresh investment, and fueling urgency ...

New hybrid quantum applications show quantum computing’s ability to optimize materials science properties using Quantum-Enhanced Generative Adversarial Networks (QGANs) and fine-tune LLM models using ...

Semiconductor processing is notoriously challenging. It is one of the most intricate feats of modern engineering due to the extreme precision required and the hundreds of steps involved, such as ...

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

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 study explores how quantum algorithms, particularly techniques in quantum machine learning, can enhance the performance of robots in tasks such as operation, navigation, and grasping. The core ...

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 ...

The researchers have explored various AI methods, including deep learning and language models, revealing the potential of these technologies in predicting system characteristics and constructing ...