Quantum computing’s promise is inching closer to reality. In the coming years, these systems will likely lead to ...

Researchers from Okinawa Institute of Science and Technology outline a strategy for using machine learning (ML) to address ...

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 ...

For over a decade, researchers have considered boson sampling—a quantum computing protocol involving light particles—as a key milestone toward demonstrating the advantages of quantum methods ...

Solving a quantum-mechanical equation called Schrodinger's equation yields this wave function. Many classical algorithms struggle to solve the correct wave function of the [4Fe-4S] molecular cluster.