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 ...

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

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 ...

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 ...

The core of the research lies in using large language models to automatically generate circuit architectures tailored to specific quantum computers. These circuit designs take into account critical ...

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

In their simulated system, image data is first simplified using a process called principal component analysis (PCA), which reduces the amount of information while preserving key features. A complex ...

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 ...

A UK startup has made a revolutionary advancement after delivering the world’s first full-stack quantum computer, built using ...