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 ...

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 ...

Reservoir computing is a promising machine learning-based approach for the analysis of data that changes over time, such as weather patterns, recorded speech or stock market trends. Classical ...

Quantum computing firm Xanadu has launched a new research initiative with defense giant Lockheed ...

A team of researchers has shown that even small-scale quantum computers can enhance machine learning performance, using a novel photonic quantum circuit. Their findings suggest that today s quantum ...

"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 ...

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 ...

Telstra has completed a trial with Silicon Quantum Computing (SQC) that sought to apply quantum machine learning to boost network automation. The 12-month trial saw the pair leverage Watermelon, SQC’s ...

In a new study published in Physical Review Letters, researchers used machine learning to discover multiple new classes of two-dimensional memories, systems that can reliably store information despite ...