D-Wave debuts a PyTorch-integrated Quantum AI toolkit, but IBM and Google's hardware breakthroughs keep the pressure on.

Banks may use it to improve risk models, while technology companies use it to create next-generation cybersecurity solutions and accelerate artificial intelligence and machine learning. IBM has been ...

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 ...

As we talked about a decade ago in the wake of launching The Next Platform, quantum computers – at least the fault tolerant ...

Quantum machine learning stands at the forefront of AI research and application, promising to address some of the most challenging problems across various industries.

What Makes Quantum Machine Learning Different QML isn’t just about running existing AI models on faster hardware. It’s about reimagining how learning itself happens.

In recent years, a number of new quantum algorithms, which hold the unprecedented potential to enhance, speed up or innovate machine learning, have been proposed. This collection invites research ...

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 ...

According to Franco Nori, "Machine learning can play a pivotal role in addressing large-scale quantum computation and optimization challenges.