In the race toward practical quantum computers and networks, photons—fundamental particles of light—hold intriguing ...

A new theory-guided framework could help scientists probe the properties of new semiconductors for next-generation ...

Recognizing the potential of these modular systems, researchers from The Grainger College of Engineering at the University of ...

CSIRO's quantum-enhanced AI model boosts chip design accuracy using only 5 qubits, outperforming classical methods in predicting GaN transistor properties.

This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools ...

So, the question that arises is, how do we use AI effectively to support their investigations? With this question in mind, a team of researchers has already started work on finding a solution for a ...

This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning to solve ...

A current approach uses classical machine learning (CML) algorithms, but they require large datasets, and their performance degrades in small-sample, nonlinear settings. The Australian researchers, ...

Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities directly from ...