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 ...

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 ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...

This illustration draws a parallel between quantum state tomography and natural language modeling. In quantum tomography, structured measurements yield probability outcomes that are aggregated to ...

The quantum tangent kernel method is a mathematical approach used to understand how fast and how well quantum neural networks can learn. A quantum neural network is a machine learning model that runs ...

WiMi's new QRAM architecture enhances quantum computing by enabling parallel data access and efficient quantum memory operations. QRAM's quantum error correction ...

In the life sciences and healthcare industries, the speed of innovation impacts how soon new products, medications and ...

This system utilizes machine learning algorithms to optimize the operation of particle accelerators, reducing manual intervention and enhancing precision in real-time control. By integrating virtual ...

A team of researchers from the University of Chicago's Pritzker School of Molecular Engineering (UChicago PME) has used Quantum Machine Learning (QML) to identify cancer early.

With Qubitra, they intend to drive practical value by applying quantum approaches to some of the finance sector’s most ...