"Machine Learning in Quantum Sciences", outcome of a collaborative effort from world-leading experts, offers both an introduction to machine learning and deep neural networks, and an overview of their ...

The computational demands of today’s AI systems are starting to outpace what classical hardware can deliver. How can we fix this? One possible solution is quantum machine learning (QML). QML ...

Quantum Machine Learning is an interdisciplinary field that harnesses the computational power of quantum systems to develop algorithms that can process and analyze data more efficiently than classical ...

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 ...

When a quantum computer processes data, it must translate it into understandable quantum data. Algorithms that carry out this 'quantum compilation' typically optimize one target at a time. However, a ...

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 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 ...

A study has used the power of machine learning to overcome a key challenge affecting quantum devices. For the first time, the findings reveal a way to close the 'reality gap': the difference between ...

As a practicing data scientist, I’ve spent years building up my library of academic and practical resources that I routinely draw upon for helping me do my work. Although my library is vast, I have a ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...