Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

MicroCloud Hologram Inc. has announced the creation of a noise-resistant Deep Quantum Neural Network (DQNN) architecture, which aims to advance quantum computing and enhance the efficiency of quantum ...

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 ...

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language translation. A quantum counterpart—known as a quantum convolutional neural ...

Integrating quantum computing into AI doesn’t require rebuilding neural networks from scratch. Instead, I’ve found the most effective approach is to introduce a small quantum block—essentially a ...

Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require.

When two-dimensional electron systems are subjected to magnetic fields at low temperatures, they can exhibit interesting states of matter, such as fractional quantum Hall liquids. These are exotic ...

RIKEN scientists tap into AI to find a smarter method for fixing quantum errors, cutting resource demands for stable quantum machines. Theoretical physicists at RIKEN have made a key advance in ...

Principal Research Fellow at AI and Cyber Futures Institute, Charles Sturt University Optical illusions, quantum mechanics and neural networks might seem to be quite unrelated topics at first glance.

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